Environmental robustness of the global yeast genetic interaction network

Single mutant fitness and gene-environment interactions

Quantitative analysis of digenic interactions depends on accurate assessment of single and double mutant phenotypes (19). Therefore, to accurately measure the effect of environment on the yeast genetic network, a baseline of quantitative measurements of condition-specific effects on single mutant fitness (GxE interactions) is required. To identify GxE interactions in the context of an SGA screen, we subjected an array of single mutants to SGA analysis using a neutral (control) “query” locus and assessed colony size following growth in different conditions. We examined 14 diverse conditions, including an alternative carbon source, osmotic stress, genotoxic stress and 11 bioactive compounds that target distinct yeast biological processes within the global genetic interaction profile similarity network (Fig. 1A,B). Relative fitness measurements were obtained for 3,704 viable deletion mutant strains and 782 temperature-sensitive (TS) alleles, corresponding to 553 essential genes, both in the reference SGA condition and in each of the 14 diverse conditions (Data File 1)(20–23).

To identify GxE interactions from our single mutant fitness measurements, we calculated a differential fitness score for each mutant by measuring the difference between mutant fitness in the reference condition versus each of the 14 conditions (fig. S1A)(23). More than half of all the genes that we examined (59%) were associated with a significant differential single mutant fitness defect (P < 0.05, differential fitness score < −0.08)(see Method Summary and (23) for details) in at least one condition (fig. S1B). The union of all genes that displayed fitness defects in each condition revealed that 6 to 8 conditions identified the majority of unique genes associated with condition-specific growth defects (fig. S1C). Additional conditions captured relatively few new genes, perhaps because different conditions can lead to a similar stress response (24). Thus, our set of 14 conditions affects a wide range of functionally diverse genes and should be useful for exploring how different environments impact overall structure and topology of the global genetic interaction network.

The total number of strains with detectable differential fitness defects in a single condition ranged from ~250 (4.5%) to ~1,200 (22%), with the largest number of condition-specific fitness defects observed in the presence of Benomyl, a microtubule depolymerizing agent (fig. S1D). Stronger negative differential fitness scores were associated with genes encoding known targets of compounds (fig. S1A). More generally, genes with differential fitness defects in a particular growth condition were often involved in a specific function perturbed by the corresponding growth condition (fig. S2, Data File 2)(20). For example, in Benomyl, numerous genes involved in mitosis or DNA replication and repair were enriched for differential fitness defects (Fig. 1C, Data File 2)(20). A number of genes involved in mRNA processing also displayed fitness defects in Benomyl, which may reflect that genes encoding α-tubulin, TUB1 and TUB3, are among the relatively few yeast genes with introns (Fig. 1C, Data File 2)(20, 25). In contrast, the differential fitness defects in Monensin, an intracellular traffic inhibitor (26), were most enriched among genes involved in vesicle trafficking, glycosylation, and cell wall biosynthesis (Fig. 1D, Data File 2)(20).

Some mutants (1,083) had positive differential fitness scores, indicating better growth relative to a wild-type strain in a particular condition compared to the corresponding mutant growth relative to wild-type in the reference condition (fig. S1A, B, D). For example, although all strains, including wild type, grew more slowly in the presence of the proteasome inhibitor, Bortezomib, strains carrying TS alleles of essential genes were enriched among the set of mutant strains that showed positive differential fitness scores in this condition (fig. S3A; P < 10⁻²⁷, Fisher’s exact test). This finding mirrors a previous observation from analysis of the global genetic interaction network where hypomorphic alleles of proteasome genes show positive genetic interactions, which likely reflects reduced degradation of the products of TS alleles, providing a growth advantage (11). Similarly, ~6-fold more TS alleles showed positive differential fitness, relative to those with a negative differential fitness, when grown in sorbitol, which may reflect a general effect of increased intracellular osmolarity (fig. S3B; P < 10⁻¹³, Fisher’s exact test)(27). In contrast, TS alleles of essential genes were depleted for positive differential fitness effects in certain conditions, including growth in the presence of the HSP90 inhibitor, Geldanamycin (fig. S3A; P < 10⁻⁴, Fisher’s exact test). This suggests that the fitness of different TS mutants is dependent on HSP90 activity, which is consistent with the known role of HSP90 as a phenotypic capacitor (11, 28–30).

Finally, we compiled a list of ~350 dynamic, “condition-responsive” genes, corresponding to the top ~5% of all tested genes based on the number of conditions in which they showed either a significant positive differential fitness score in 2 or more conditions or a significant negative differential fitness score in 4 or more conditions (Data File 1)(20). Consistent with our findings, condition-responsive genes identified in our assay were also often sensitive to multiple chemicals when assayed as homozygous gene deletion mutants (fig. S3C; 2.7-fold, P < 10⁻¹², Fisher’s exact test)(31). Moreover, condition-responsive genes were more likely to be essential or to have a significant growth phenotype as a deletion mutant in the reference condition, were often highly connected on the global yeast genetic interaction network, and often encode highly conserved, multifunctional proteins (fig. S3D).

Mapping genetic interactions across different environments

Our systematic assessment of GxE interactions involving single mutants provides a foundation for comprehensive analysis of how genetic interactions are modulated by environment (GxGxE interactions). To do this, we took advantage of a diagnostic array used previously to survey complex, trigenic interaction networks (Data File 1)(6, 20). This array consists of ~1,200 strains, comprising ~1,000 nonessential gene deletion mutants and ~200 TS alleles of essential genes, spanning ~20% of all yeast genes and representing the functional breadth of the yeast genome (Data File 1)(6, 20). We note that mutant strains included on the diagnostic array exhibited a range of fitness defects and are generally representative of the genome-wide distribution of differential single mutant fitness effects (Data File 1)(20). For condition-specific genetic interaction analysis, we selected 26 query genes, each of which shows a substantial number of genetic interactions in the global network (11) and a functionally coherent genetic interaction profile that localizes the query gene to a specific biological process cluster on the reference genetic interaction similarity network (Fig. 1B).

We crossed the 26 query strains to the diagnostic mutant array resulting in ~30,000 unique double mutant strains, which were each assessed for environmental modulation of genetic interactions in all 14 conditions. To measure condition-dependent genetic interactions, every double mutant array generated from a single query gene was screened 3 times. One copy was grown in the standard SGA condition, while the two other copies were each grown in different conditional media (fig. S4). This configuration provided a matched reference control for every condition, which facilitated normalization of systematic experimental artifacts and improved accuracy of condition-specific genetic interaction measurements (32). The entire screening pipeline was repeated twice, providing two independent biological replicates for every query gene in every test condition, each one with 4 separate double mutant colonies (8 tests of each double mutant per condition), and 14 independent biological replicates of each query screen in the standard reference SGA condition (23).

Negative and positive genetic interactions were quantified as previously described (19). We identified an average of ~2,100 negative and ~1,400 positive genetic interactions on the basis of 14 biological replicate screens performed in the standard, reference condition, at an intermediate confidence threshold (fig. S5A, Data File 3)(20, 23). A similar number of gene pairs exhibited a genetic interaction in any single test condition, with an average of ~2,150 negative and ~1,500 positive genetic interactions identified per condition (fig. S5A). Our genetic interaction measurements were reproducible, and those interactions identified in the reference condition in this study overlapped substantially with interactions derived from our previous genome-wide study (fig. S5B)(11). Comparison of the overlap of genetic interaction profiles between a specific condition screen and its matched control to that seen with an unmatched control suggested that comparison to a matched reference provides the necessary sensitivity to detect rare condition-specific interactions (fig. S5C, Table S1-S2, Data File 3)(20), consistent with previous studies (32).

Quantifying and classifying differential interactions

To discover differential or GxGxE interactions in our scored data, we quantified the difference between genetic interaction scores derived from each conditional screen and its corresponding matched reference (fig. S4)(23). We scored a differential negative interaction when a genetic interaction score observed in a particular condition was less than the genetic interaction score measured in the corresponding reference control. Conversely, a genetic interaction that was stronger in a given condition compared to the matched reference control was scored as a differential positive interaction. Analysis of biological replicates confirmed the reproducibility of differential interaction scores and enabled scoring of dynamic, differential genetic interactions while controlling for the false discovery rate (fig. S5D, Table S1-S2)(23).

The Benomyl screen showed the largest number of differential, condition-specific fitness defects and genetic interactions, providing a rich context for developing a general scheme for defining various classes of differential interactions. We measured Benomyl differential interactions as described above, adopting an intermediate confidence threshold (|ε| > 0.08, P < 0.05, see Method Summary and (23) for details)(Fig. 2A, left panel). In total, we identified 1,367 differential interactions in Benomyl, which was ~3-fold less than the 3,845 genetic interactions identified in the matched reference condition (Fig. 2B). Thus, the majority of the genetic interactions observed in the reference condition were also observed in the presence of Benomyl. To determine if the prevalence of Benomyl differential interactions derived from the diagnostic array was generalizable to the whole genome, we also screened the same set of 26 SGA query mutant strains against the complete collection of nonessential gene deletion mutants and essential gene TS alleles, in the absence or presence of Benomyl (fig. S6A-B, Data File 3)(20, 23). This analysis also revealed ~3-fold fewer differential interactions relative to genetic interactions indicating that trends observed with the diagnostic array accurately reflect an unbiased genome-scale analysis (fig. S6C).

In an effort to discover what new functional information might be associated with differential interactions, we divided them into four classes: reversed, modified, masked and novel (Data File 3)(20, 23). These classes of differential interactions can be depicted schematically for those with either negative (Fig. 2A, right panel) or positive (fig. S7A) scores. The reversed class was the rarest and included gene pairs that showed significant but opposite genetic interactions in a condition versus the matched reference, accounting for less than 1% of all Benomyl differential interactions (Fig. 2B). Often these differentials involved at least one relatively weak interaction, which means they are likely more prone to false positives and false negatives. Thus, the sign of a fitness-based genetic interaction does not frequently change in an altered environment. The remaining classes were roughly equivalent in size and accounted for the majority (> 99%) of all Benomyl differential interactions (Fig. 2B, Data File 3)(20). We identified 538 (538/1,367; ~39%) novel differential interactions, which were not observed as genetic interactions in the reference network. The remaining (829/1,367; ~61%) differential interactions were either modified or masked, meaning that they were scored as genetic interactions in a particular condition as well as in the reference control, but differed in relative strength, and thus were modulated by the environment. Genome-wide analysis revealed similar fractions of modified, masked and novel Benomyl differential interactions (fig. S6D).

Analysis of specific examples of modified differential interactions from the Benomyl screens revealed that modified differential interactions could arise in two ways (Fig. 2A, fig. S7A). First, a genetic interaction detected in the reference network may be exacerbated in a specific condition, resulting in a significant differential score. For example, a negative genetic interaction between MYO2 and DYN2 was stronger in Benomyl, reflecting the importance of these two motor proteins in nuclear positioning and spindle orientation, especially when microtubule function is compromised (Fig. 2B). In another example, a positive genetic interaction between the proteasome gene, RPN12, and TCP1, which encodes an essential subunit of the chaperonin-containing T-complex involved in tubulin folding (Fig. 2B), was enhanced in the presence of Benomyl. In this case, compromising proteasome function may impair the degradation of the TCP1 TS allele product, which is particularly important in the presence of Benomyl.

Second, a negative differential score may arise when a positive genetic interaction in the reference network is weakened in a specific condition (Fig. 2A, fig. S7A). For example, a positive genetic interaction between GIM3 and RPN12, which encode subunits of the Prefoldin chaperone complex and the 19S proteasome, respectively, was weaker in the presence of Benomyl, resulting in a negative differential score (Fig. 2B). Similarly, a positive differential score can result from a condition-specific change in magnitude of a negative genetic interaction. For example, in the reference condition, GIM3, which is involved in tubulin folding, showed a relatively strong negative genetic interaction with DAD1, an essential kinetochore gene (Fig. 2B). In Benomyl, the GIM3-DAD1 negative genetic interaction was significant but much weaker, resulting in a differential positive interaction (Fig. 2B), which may reflect that Benomyl perturbs the microtubule cytoskeleton in a manner that encompasses the cell physiology associated with the GIM3-DAD1 interaction.

We also categorized a specific subset of modified differential interactions, which we call ‘masked’, where positive or negative genetic interactions were only identified in the reference condition (Fig. 2A, fig. S7A). For example, while a negative genetic interaction was scored in the reference condition for GIM3 and TUB3, which encodes alpha-tubulin, no genetic interaction was observed in the presence of Benomyl, leading to a GIM3-TUB3 differential positive interaction (Fig. 2B). Positive genetic interactions scored in the reference condition can also be masked. For example, a positive genetic interaction between the yeast actin gene, ACT1, and the microtubule motor encoding gene, KAR3, was no longer detectable in Benomyl, leading to an ACT1-KAR3 differential negative interaction (Fig. 2B).

Although relatively rare, novel differential interactions are particularly interesting because they are revealed only in a specific condition but not in the reference condition, and thus should reflect functional links between genes that are driven by condition-dependent cellular physiology (Fig. 2A, fig. S7A). For example, we identified a novel negative differential interaction between DSN1 and KIP3 in Benomyl highlighting a functional link between the kinetochore and a microtubule motor protein involved in spindle assembly (Fig. 2B).

Multiple environments, differential interactions, and the reference genetic network

We applied our scoring and classification system for differential genetic interactions, described above, to all 14 conditions surveyed. On average, less than 3% of all gene pairs tested in any given condition showed a differential interaction as compared to ~13% of all gene pairs that exhibited a genetic interaction in the reference condition, consistent with the percent of overlapping gene pairs that show a genetic interaction in the context of a genome-wide study (fig. S7B-C, Data File 3)(11, 20). Comparison of negative interactions, in particular, revealed a striking difference where ~1% of tested gene pairs exhibited a significant differential negative interaction, relative to ~8% of all gene pairs that showed a negative genetic interaction in any single condition tested (Fig. 3A, S7C, Data File 3)(20). Importantly, two-thirds of all differential interactions were classified as modified or masked because they overlapped a genetic interaction in the reference condition (Fig. 3B). On average, modified and masked differential interactions from a single condition accounted for ~14% of all genetic interactions in the reference network (Fig. 3B). Depending on the condition, we estimate that between ~5–24% of genetic interactions detected in a reference genetic network can be modulated in a different environment.

Novel differential interactions provide a direct estimate of the additional functional information that different environments can contribute to a genetic network. On average, when compared to a matched reference control, ~7% of genetic interactions identified in a single condition were classified as novel differential interactions (Fig. 3B, Data File 3)(20). Thus, the vast majority (~93%) of the genetic interactions mapped in different environmental conditions were also on the reference network. Novel differential negative interactions were also significantly weaker in magnitude compared to genetic interactions measured in the reference condition (Fig. 3C). Detailed analysis of previously reported differential interaction networks (14, 15, 17) confirmed that differential interactions, in particular, novel differential interactions, were much less abundant than genetic interactions (fig. S7D).

It is critical to account for the false negative rate associated with high-throughput genetic interaction screens because failure to detect a true reference condition genetic interaction can be mistakenly classified as a novel differential interaction. We applied a Markov Chain Monte Carlo (MCMC)-modelling approach to generate a robust consensus set of negative and positive reference condition genetic interactions for each of the 26 SGA query genes based on the collection of 14 independent, biological replicate screens (23). The resultant consensus genetic interaction profiles were used as a gold-standard to estimate false discovery and false negative rates at defined confidence thresholds (Table S1-S2; Data File 4)(20). From analysis of reference condition replicate screens, we estimated false negative rates of 39% and 52% for negative and positive genetic interactions, respectively (Table S1)(23). In the case of Benomyl, we mapped 538 novel differential interactions when compared to a reference condition network derived from a single, matched control (Fig. 3D, fig. S7C). However, a more rigorous comparison of Benomyl genetic interactions versus the MCMC consensus reference genetic interaction profile identified 454 novel differential interactions or ~15% fewer interactions than comparison to a matched control screen (Fig. 3D). A third comparison using a saturated reference network, on the basis of a reference network derived from the union of all the control screens (fig. S7E), which is less prone to false negative interactions, identified 319 novel differential interactions, or ~40% fewer interactions compared to those identified using a single matched reference control (Fig. 3D)(23). Indeed, the number of novel differential interactions could decrease by as much as ~60% depending on the condition and whether the reference was based on a single matched control, a consensus network control (23), or the union of reference control screens (Fig. 3D; Data File 4)(20). Our analysis highlights the importance of a robust reference network, replicate screens and rigorous estimates of false negative rates for comparative study of genetic interactions in an alternative environmental condition.

Properties of differential interactions

Comparing all 14 conditions to their matched controls identified a combined total of ~10,000 differential interactions, the majority (61%) of which were unique to a single growth condition, with differential negative interactions being less prevalent and relatively weaker than differential positive interactions (Fig. 3B, fig. S7F). Genes that were highly connected hubs on the global genetic network were also more likely to be hubs on a differential network, since the average number of differential interactions for an individual array gene, across all 14 conditions, was significantly correlated to the interaction degree in the global genetic network (Pearson correlation, r = ~0.7, P < 10⁻¹⁶, fig. S8A, B). The number of novel differential interactions associated with each query mutant examined in this study was also correlated to interaction degree in the global genetic network (Pearson correlation, r = ~0.5, P < 0.006, fig. S8C), suggesting that the frequency of novel differential interactions observed using a diagnostic set of genes should reflect the genome-wide prevalence of novel differential interactions. Consistent with these observation, gene features associated with hubs on the reference network were shared with differential network hubs (fig. S8D)(11). Notably, high novel differential interaction degree was associated with genes whose loss-of-function resulted in a single mutant fitness defect in the reference condition (fig. S8E), and genes with condition-dependent fitness defects had proportionally more novel differential interactions than genes lacking condition-dependent fitness defects (fig. S8F). Like genetic interaction hubs, genes with many novel differential interactions were associated with multiple Gene Ontology (GO) annotations, and lower dN/dS suggesting that they are more functionally pleiotropic and tend to be under stronger evolutionary constraints (fig. S8D-E). Conversely, transcript levels of high differential interaction degree genes did not vary substantially across different environments or genetic backgrounds, suggesting that environmentally responsive gene expression patterns are not generally predictive of differential interactions (fig. S8D).

Negative genetic interactions measured in any condition tended to connect functionally related gene pairs and overlapped with other types of molecular interaction networks (Fig. 4A-B). Differential negative interactions also identified functionally related gene pairs, but to a lesser extent (Fig. 4A-B). Closer examination revealed that most of the functional signal associated with differential negative interactions was captured by the modified class, whereas those belonging to the novel or masked categories did not overlap substantially with other molecular interaction datasets (Fig. 4B). Thus, negative genetic interactions that occurred in a condition-specific manner (i.e. novel differential negative interaction; Fig. 2A), and positive genetic interactions that are masked in a particular condition (i.e. masked differential negative interaction; Fig. 2A) often involve gene pairs with unrelated functional annotations.

Consistent with previous observations (11), positive genetic interactions identified in either the reference or alternative conditions were less functionally informative than negative genetic interactions (Fig. 4A, C). However, the complete set of differential positive interactions appeared to connect functionally related genes more often than positive genetic interactions measured in either the reference or individual test conditions (Fig. 4C). Modified and masked differential positive interactions were the most predictive of functionally related gene pairs, often connecting members of the same protein complex and overlapping significantly with protein-protein interactions (Fig. 4B). Further analysis revealed that the functional signal associated with the masked and modified classes was largely attributable to gene pairs that displayed negative genetic interactions in the reference condition but were either weaker (modified) or no longer detectable (masked) in a particular condition (Fig. 4D). This may reflect a particular environmental perturbation that mimics the cellular physiology associated with a double mutant strain grown in the reference condition, obscuring a phenotype associated with a genetic interaction. Thus, while modified and masked differential positive interactions tend to connect functionally related gene pairs, this same information is captured by negative genetic interactions identified in the reference condition. We note that genome-wide analysis revealed similar functional trends associated with Benomyl differential negative and positive interactions (fig. S6E-F).

To further explore the functional information associated with condition-specific interactions, we grouped together array genes that belong to the same biological process cluster represented on the global genetic profile similarity network (Fig. 1A) and measured how often each query gene showed either genetic or novel differential interactions with each functional group (Fig. 5, Data File 5)(11, 20, 23). Query and array genes within the same bioprocess cluster were often connected by negative genetic interactions in the reference condition (~3.6-fold enrichment within bioprocess, Fig. 5A, on-diagonal). For example, in the reference condition, the VTI1 query gene, which encodes an essential SNAP receptor (v-SNARE) involved in multiple protein sorting pathways (33–35) and located in the vesicle traffic bioprocess cluster, showed strong enrichment for negative genetic interactions with functionally-related array genes located in the same vesicle traffic bioprocess cluster (Fig. 5B).

In contrast, novel differential negative interactions did not connect query and array genes annotated to the same biological process (Fig. 5C). While VTI1 showed the most novel differential interactions of any query gene tested (Data File 3)(20), most of these interactions did not involve other vesicle traffic-related genes. Instead, we observed modest but significant enrichment for novel differential negative interactions between the VTI1 query gene and array genes with roles in cell polarity and nuclear transport (Fig. 5D). Indeed, the vast majority (1489/1553, ~96%) of all novel differential negative interactions identified using a matched reference control connected pairs of genes located in different bioprocess clusters on the global similarity network. These results further indicated that condition-specific genetic interactions do not identify gene pairs with a close functional relationship in the same general bioprocess but rather have the potential to uncover weaker functional associations between distinct biological processes.

Differential interactions capture distant but coherent functional relationships

We next explored the functional distribution of novel differential interactions across each of the 14 different conditions. In particular, we tested if array genes annotated to the same function showed more novel differential interactions in a particular condition (Fig. 6). Array genes annotated to specific biological processes were enriched for novel differential negative interactions in response to a specific condition that perturbs the same bioprocess (~2.3-fold enrichment within bioprocess; Fig. 6A, on-diagonal). For example, consistent with previous observations (14, 15), we found that array genes in the DNA replication and repair bioprocess cluster were enriched for novel differential negative interactions in the presence of MMS (3.9-fold, Fig. 6B). But as shown above (Fig. 5), these novel differential interactions did not involve related query genes with roles in DNA replication and repair. The enrichment observed among DNA replication and repair array genes was predominantly driven by MMS-specific, novel differential negative interactions with the MYO2 query gene, a type V myosin motor involved in actin-based vesicle transport and spindle orientation, and with the VTI1 and TRS20 query genes involved in vesicle transport, highlighting a functional link between DNA replication and vesicle trafficking (Fig. 6B)(33–35). In another example, vesicle traffic genes were enriched for novel differential negative interaction in response to Monensin (2.5-fold, Fig. 6A). In this case, Monensin-specific negative interactions connected vesicle traffic array genes to the RSP5 query gene, which encodes a ubiquitin ligase involved in multi-vesicular body sorting, the heat shock response, endocytosis, and ribosome assembly (36), as well as the LSM6 query gene, which has a general role in RNA processing (Fig. 6B)(37, 38). Novel differential positive interactions were also not enriched among functionally related genes, and they were less informative of gene function than novel differential negative interactions (fig. S9A, Data File 5)(20).

A similar trend was observed in genome-scale Benomyl screens, where novel differential negative interactions specifically connected array genes with mitosis-related roles to functionally diverse query genes, such as NUP188, which encodes a nuclear pore component (39), and GPI15, involved in glycosylphosphatidylinositol (GPI) anchor biosynthesis (fig. S9B, Data File 5)(20, 40, 41). Thus, environment may sensitize genes with roles in a specific bioprocess to negative genetic interactions with functionally diverse query genes.

In summary, the majority of differential interactions overlap a genetic interaction identified in the reference condition, which often connect functionally related genes within the same biological process. The environmental rewiring of genetic networks is driven by rare condition-specific, and relatively weak novel differential interactions, which identify new connections between genes with diverse functions in different biological processes (Fig. 7).

Discussion

We surveyed a set of functionally diverse yeast genes and different environments, quantifying how the growth phenotypes associated with different single gene mutations were modulated by the environment (GxE), and how the growth phenotypes associated with different genetic interactions (GxG) respond dynamically to a particular condition to generate differential interactions (GxGxE). Our general findings reveal how environmental conditions modulate the yeast global genetic interaction network, allowing us to assess the plasticity of genetic networks, and the extent to which mapping genetic interactions in different environments can expand a reference network and generate new functional information.

In general, genetic interactions, especially negative genetic interactions, are rich in functional information because they tend to connect genes that function within the same biological process (GxG, Fig. 7)(11). Analogously, if an environmental perturbation affects a particular biological process, then genes with roles in the perturbed bioprocess tend to show differential sensitivity in the corresponding condition (GxE, Fig. 7). Most genetic interactions are not modulated by the environment and remain detectable in both a given test condition and the reference control condition. However, subsets of genetic interactions can be modified by a particular condition, leading to a differential interaction (GxGxE, Fig. 7). Differential interactions are relatively rare because, on average, we observed ~3-fold fewer differential interactions compared to genetic interactions detected in the reference condition.

Most differential interactions belonged to the modified or masked classes, which overlapped with a negative or positive genetic interaction in the reference genetic network. The majority (~70%) overlapped specifically with negative genetic interactions and thus were functionally informative, connecting genes belonging to the same general biological process (GxGxE – e.g. modified, Fig. 7). However, because they recapitulate connections that are captured in the global genetic network mapped in the reference condition, modified and masked differential interactions do not contribute new functional information. On the other hand, novel differential interactions correspond to condition-specific genetic interactions between genes that do not interact in the reference condition. The rare sub-class of novel differential negative interactions does not typically include functionally related gene pairs. Instead, these interactions tend to connect groups of genes that are sensitive to a particular environmental perturbation to functionally distant genes (GxGxE – novel, Fig. 7). Thus, novel differential interactions highlight new connections between distinct cellular functions and, as a result, have the potential to expand upon the global genetic network.

Although detecting novel differential interactions promises to add new information to genetic networks, an accurate definition of novel differential interactions is challenging as it depends on the quality and comprehensiveness of the reference genetic network. For example, multiple independent replicate screens reduce the number of false negative interactions observed in the reference condition but consequently, also decrease the number of differential interactions classified as novel upon environmental perturbation (Fig. 3D). Indeed, the relative fraction of novel differential interactions contributed to the reference network by a single environmental condition can vary widely when using a single, matched control (~7%) or the union of all available replicate controls (~1%)(Data File 4)(20). Using a high confidence consensus reference network (Data File 4)(20), we estimate that screening one additional environmental condition contributes less than ~4% (191/5174) novel interactions relative to the reference network, suggesting that most genetic interactions are captured in a single condition. Hence, while differential interaction analysis in multiple diverse conditions reveals the subset of genetic interactions that are modulated upon environmental perturbations (i.e. modified or masked interactions), they can only marginally expand the size of the network (i.e. novel interactions) highlighting that the global genetic interaction network is generally robust to environmental perturbations.

While our study involved the use of a diagnostic array of yeast genes, selected environmental conditions and functionally diverse query genes, several observations suggest that our results capture the general resilience of the global yeast genetic interaction network to environmental perturbation. First, our genome-scale single mutant fitness analyses suggested that the selected set of conditions and small molecules elicited widespread but distinct cell physiological effects. Second, a genome-wide comparison of genetic interactions measured in the absence and presence of Benomyl yielded a similar fraction of differential interactions seen using a diagnostic mini-array of genes. Finally, analysis of several independent interaction datasets, derived from SGA-based approaches using various subsets of yeast genes and a number of different conditions, including various DNA damaging agents and stress-response conditions, confirmed that differential interactions are substantially less prevalent than the genetic interactions observed in the reference condition (fig. S7D) (14, 15, 17).

Consistent with our results, previous surveys of subsets of genes involved in a specific cellular function perturbed by a particular condition revealed an enrichment for differential interactions that often occurred between functionally distant gene pairs (14, 15, 17). We note that, although included in previous studies, a subset of differential interactions, based on statistically insignificant genetic interaction scores, was omitted from our analysis (23). However, these particular differential interactions were not enriched among functionally related genes and did not appreciably increase the total number of differential interactions relative to the size of the reference genetic interaction network examined here or reported in other studies (fig. S7D, S10). Thus, in general, genetic interactions between the vast majority of genes and their corresponding functional modules (e.g. complexes and pathways) are not dynamic or rewired in response to environmental stimuli. Moreover, we found that the ~20% of yeast genes with relatively sparse genetic interaction profiles (11) are statistically depleted for condition-specific fitness defects (~3-fold depletion, P < 10⁻⁹⁹, Fisher’s exact test, one-tailed). The relationship between fitness and interaction degree (fig. S8) along with the strong correlation observed between interaction degree for a given gene in the global genetic and differential networks (fig. S8), further suggests that condition-specific genetic interactions will not appreciably increase the number of interactions associated with low degree genes in the global reference genetic network.

Differential interactions reflect the phenotypic consequences of combining three independent perturbations, two genetic and one environmental perturbation, and thus conceptually resemble trigenic interactions, which involve three independent genetic perturbations, especially if a condition involves a drug with a highly specific cellular target. As a result, differential and trigenic interaction networks share several properties in common: [1] As observed for differential interactions, the average trigenic interaction degree for a given gene was correlated to its connectivity in the global genetic network (6); [2] Like novel differential interactions, trigenic interactions occur at similar reduced frequencies relative to digenic interactions in a reference condition (fig. S7D); [3] Like modified and masked differentials, a substantial proportion of trigenic interactions overlapped with and exacerbated digenic interactions previously observed in the global digenic network (6); [4] As observed for novel differential interactions, novel trigenic interactions are also relatively rare and weaker, but highly coherent in that they often involve array genes from the same biological process; however, and they are also substantially more functionally diverse than digenic interactions (6).

GxE and GxGxE interactions involving the natural variation of different yeast strains can also be interpreted in the context of the global genetic interaction network and the properties of novel differential interactions. Using segregating populations of natural yeast isolates, recent surveys of GxG and GxGxE interactions between a deletion mutant and natural genetic variants showed that GxG interactions tend to connect genes within the same cellular function (42), whereas GxGxE interactions tend to be specific to a particular condition (43), and they often connected distantly related genes (44).

Quantitative trait loci (QTL) studies, focused on the fitness variation of different segregants derived from yeast crosses in different environments, showed that the majority of the phenotypic variation can be explained by single-locus effects (GxE), whereas epistatic genetic interactions, analogous to GxGxE interactions, are relatively rare (4, 45), and tend to impact only individuals with extreme phenotypes (5). Based on our analyses, most GxGxE interactions involve genes with single mutant differential effects (GxE) (fig. S8E), which should be analogous to single-locus effects identified in QTL analyses and contribute to both the additive and epistatic variance. In other words, the low epistatic variance often seen in QTL analyses may be partly explained by the partial contribution of interacting loci to the additive variance component (46). As a result, the remaining epistatic effect may only be visible in individuals with the most extreme phenotypes (5). Given that most GxGxE interactions overlap a genetic interaction in the global genetic interaction network, applying principles and gene modules inferred from a global network should allow for the detection of coherent epistatic interactions with relatively small effects that cannot be recovered using QTL analyses (47, 48).

We conclude that the yeast genetic interaction map derived from a single reference condition is highly robust to environmental influences because most connections on the global genetic interaction network remain unchanged or unmodified in a new environment. Although each new condition has the potential to map a relatively small number of novel differential interactions, the global digenic network mapped in a single condition is informative of differential interactions (fig. S8A-B) and should serve as an accurate and representative reference network. Nonetheless, differential interactions can reveal new functional connections between distantly related genes and bioprocesses. Given the significant association between the differential single mutant fitness defect and the frequency of differential interactions for a given gene, a logical and efficient strategy may involve mapping differential interactions for specific genes required for normal growth in a environment of interest (i.e. stress condition, drug treatment, microbial exposure etc.).

While environment has a modest impact, cell type-specific gene regulation has the potential to substantially complicate genetic network analyses in more complex systems. However, genome-wide studies suggest that only ~8,500 genes (~45–49%) are expressed in any given cancer cell line and that a “core set” of ~7,700 genes are expressed in the majority of all cancer cell lines examined to date (49). Thus, a systematic survey of genetic interactions based on a single cell line grown in one environment should be sufficient to map a global reference genetic network that encompasses this core set of expressed genes and provide a basic scaffold for the genetic wiring of a human cell. As observed in yeast, a global genetic interaction network for a model human cell line should reveal a hierarchy of functional connections among genes (1, 11), and expansion of this network to specific cell types would enable the deciphering of mechanisms underlying cancer cell-specific genetic vulnerabilities (50, 51). Ultimately, reference genetic networks mapped in human cells should facilitate interpretation of allelic combinations of genes underlying inherited traits (3, 47, 48).

Methods Summary

Conditions

A list of 14 conditions and concentrations used in this study are provide in Data File 1(20).

Supplementary Material