A comprehensive quantitative genetic interaction map, or E-MAP, has provided a global view of the functional interdependencies between the components of the transcriptional apparatus in budding yeast.Transcription factors that display aggravating/negative genetic interactions regulate gene expression in an independent rather than coordinated manner.Parallel/compensating relationships between regulators often characterize transcriptional circuits.
Genetic interactions identify the functional interdependencies between genes (Guarente, 1993). They can be either positive (i.e. alleviating) or negative (i.e. aggravating) in nature corresponding to cases where the double mutant grows better or worse, respectively, then expected from growth of the corresponding single mutants (Beyer et al, 2007). Negative genetic interactions between non-essential genes often identify factors involved in parallel pathways, whereas positive ones often correspond to cases where the corresponding proteins are working in the same pathway and/or are physically associated (Beltrao et al, 2010). The epistatic miniarray profile (E-MAP) approach (Schuldiner et al, 2005), which quantitatively and comprehensively identifies both positive and negative genetic interactions on a logically selected set of genes, was used in this study in S. cerevisiae to genetically interrogate the set of 151 sequence-specific transcription factors (STFs) as well as 172 components of the general transcriptional machinery (GTFs).
We found a higher propensity of the group of STFs to strongly genetically interact with GTFs than with themselves (Figure 1A and B). However, within the set of STF–STF genetic interactions, there was a significant enrichment of negative over positive genetic interactions (Figure 1A and C), suggesting that parallel/compensating relationships, rather than linear pathways, predominate within the set of STFs. These genetic trends are in stark contrast to what was previously observed with factors involved in regulating signaling (e.g. kinases and phosphatases), which were significantly enriched in positive over negative genetic interactions (Fiedler et al, 2009).
In addition to providing an overview of the global relationships among TFs, the fine structure of the E-MAP can be used to address the nature of the regulatory architecture controlling individual genes. A variety of regulatory patterns have been described that serve the differing functional requirements of various biological processes (Istrail and Davidson, 2005). Our E-MAP identified several examples of the regulatory relationships between transcription factors, including (1) one TF acting as a repressor of another TF (e.g. Gal80 acting as a repressor of Gal4, the activator of the GAL genes); (2) two TFs acting redundantly to regulate a set of genes (e.g. Gln3 and Gat1, two GATA family activators involved in regulating nitrogen catabolite repression (NCR)); and (3) two TFs regulating genes in a coordinated manner (e.g. Hac1 working with the HDAC complex Rpd3C(L) to regulate expression of early meiotic genes).
Given the complex structures of promoters (Zhu and Zhang, 1999; Chin et al, 2005) and the possible types of regulatory logic (Buchler et al, 2003), we wanted to identify the types of logic that are used in nature. We explored this by combining our genetic interaction data with the information about the network connections between STFs and their targets. By initially focusing on pairs of STFs that share a set of targets defined by the genome-wide binding studies (Harbison et al, 2004; MacIsaac et al, 2006), a total of 110 STF gene pairs were identified that have statistically significant target overlap with a P-value <0.005, whereas 49 pairs have significant overlap at a more stringent cutoff (P<10−7). Several examples were examined in more detail by quantitative growth assay in liquid culture and gene expression profiling of the TF-deletion mutants. In each case, the growth rate of one of the single-deletion mutants is significantly reduced (i.e. ‘the major regulator'), whereas the growth rate of the other single-deletion mutant is similar to that of the wild type (i.e. ‘the minor regulator'). In the absence of the major regulator, the deletion of the minor regulator leads to a more severe growth defect, resulting in a negative genetic interaction (Figure 5A). We examined the response of common target genes of two pairs of TFs (Swi4-Skn7 and Gcr2-Tye7) and found an enrichment of common target genes displaying ‘OR' but not ‘AND' behavior, in the simplified language of Boolean logic. Further examination of the targets revealed that many of them are induced/repressed more by the double deletion than each of the single deletions (Figure 5D). Collectively, these results suggest that frequently TF pairs with negative interactions regulate the transcription of their common target genes in a redundant manner.
The regulation of gene expression is, in large part, mediated by interplay between the general transcription factors (GTFs) that function to bring about the expression of many genes and site-specific DNA-binding transcription factors (STFs). Here, quantitative genetic profiling using the epistatic miniarray profile (E-MAP) approach allowed us to measure 48 391 pairwise genetic interactions, both negative (aggravating) and positive (alleviating), between and among genes encoding STFs and GTFs in Saccharomyces cerevisiae. This allowed us to both reconstruct regulatory models for specific subsets of transcription factors and identify global epistatic patterns. Overall, there was a much stronger preference for negative relative to positive genetic interactions among STFs than there was among GTFs. Negative genetic interactions, which often identify factors working in non-essential, redundant pathways, were also enriched for pairs of STFs that co-regulate similar sets of genes. Microarray analysis demonstrated that pairs of STFs that display negative genetic interactions regulate gene expression in an independent rather than coordinated manner. Collectively, these data suggest that parallel/compensating relationships between regulators, rather than linear pathways, often characterize transcriptional circuits.