In this study we scored differential expression at the level of gene sets to infer changes in the activity of transcription factors from the mRNA expression levels of the genes predicted to be under their control, based either on upstream sequence matches to cis-regulatory elements (motif-based regulons) or on occupancy by a specific transcription factor (ChIP-based regulons). We created a database of inferred regulatory activities for a large number of TFs under a wide variety of stress conditions and gene deletion mutants in budding yeast, and used it to perform TF-centric analysis of the yeast regulatory network.
Whether the ChIP-based or motif-based regulon performs better depends on the identity of the TF and possible also the expression profile analyzed. It is difficult to make a general statement. However, the t-values reported by our website make it easy for the user to compare the performance for any TF/experiment combination of interest.
We have validated our computational approach both computationally and experimentally. First, we confirmed that deletion and over-expression of two transcription factors (an activator and a repressor) resulted in the expected up- and down-regulation of their accompanying gene groups. Second, using fluorescence microscopy we were able to observe the translocation of two transcription factors to the nucleus during calcium and DTT treatment, in agreement with the T-profiler predictions.
DTT stress also activates a specific response of a gene group regulated by the Hac1p transcription factor, a response that does not occur in cells treated with calcium. In fact, querying our database for experiments, in which the Hac1p-based gene group is activated, only revealed 11 experiments with significant t-values. Four of those originate from the DTT time course, while the others are from transcription profiles of partially suppressed essential genes. Interestingly, these genes are either involved in GPI-anchor biosynthesis, GPI-anchor addition, or in GPI-protein maturation. Another example is that the Rlm1p-based gene group is mainly activated in experiments related to cell wall perturbation, caused by, for example, Calcofluor white or Zymolysase 
, or in deletion mutants defective in cell wall formation 
. Such use of our database to query for condition specific activation bears some resemblance to the “connectivity map” approach 
, which related a compendium of drug related gene expression signatures (represented as gene sets) to the expression profiles of gene deletions and disease.
To further analyze functional relationships between TFs, we used inferred activity TF profiles across a large number of conditions to organize TFs into a “co-modulation network” consisting of a number of disjoint sub-networks. In agreement with the results of Luscombe et al. 
we found the cell-cycle and pheromone sub-network to be separated from the other sub-networks. The advantage of inferring TF activities as hidden variables was illustrated for the transcription factors Mbp1p and Stb1p, which show poor correlation at the mRNA level but strong correlation at the regulon activity level. Recognizing that such correlation might be caused by overlap between the regulons, we removed the 23 genes that occurred in both regulons and recomputed the correlation, which remained high. Tomlins et al
were able to use a method purely based on the overlap between gene groups from various sources to build an interaction network that yielded new insights on prostate cancer progression. This suggests that while our co-modulation network approach provides useful biological information about TF-TF associations even if there is no overlap between regulons, the contribution to the regulon-regulon correlation from the overlapping genes is also biologically meaningful.
In contrast to the condition-specific activity of many regulons, those based on the STRE motifs (AGGGG/CCCCT) and TBP (TATAWAWR) are activated in 50% of all conditions and are therefore regulated in a more general manner. Compared to the STRE and TBP-regulons, the PAC and rRPE regulons show opposite transcriptional behavior. The observed bipolar transcriptional regulation in Saccharomyces cerevisiae
is also found by others 
.We propose that there is a mechanistic relationship between the regulation of these motif gene groups and provide evidence that NC2, a bi-functional transcriptional regulator that binds TBP, could serve as the mechanistic link. Basehoar et al
showed that approximately 20% of yeast genes contain a TATA box, and similar numbers have also been found for higher eukaryotes 
. It might be interesting to determine to what extent this form of regulation is conserved in higher eukaryotes.
While the results reported here are limited to the yeast S. cerevisiae, we expect our approach to be valid in other organisms as well, including human. Whenever prior information about which genes are directly targeted by a TF is available, regulon-based analysis of differential expression using T-profiler should allow the “hidden variables” that represent the true post-translational activity of the TF to be estimated from the genomewide expression profile.