Comparative cofactor screens reveal the influence of transactivation domains and core promoters on the mechanisms of transcription [ZFP-TAD]
Eukaryotic transcription factors (TFs) activate gene expression by recruiting cofactors to promoters. However, the relationships between TFs, promoters and their associated cofactors remain poorly understood. Here, we combine GAL4-transactivation assays with comparative CRISPR-Cas9 screens to identify the cofactors required by nine different TFs in human cells. Using this dataset, we associate key TFs with their cofactors, classify cofactors as ubiquitous or specific, discover novel transcriptional co-dependencies and demonstrate that certain TFs use the tail 2 and kinase submodules of Mediator to potentiate transcriptional elongation. By employing a reductionistic and comparative approach, we demonstrate that TFs do not display discrete mechanisms of activation. Instead, each TF is dependent on a unique combination of cofactors, which influences distinct steps in the transcriptional process. We also extend our screens to nine different core promoters to explore how core promoter elements influence cofactor dependence. Our data suggest that different classes of promoter are constrained by either initiation or pause release, which influences their dynamic range of gene expression and compatibility with specific cofactors. Overall, our comparative cofactor screens uncover the interplay between TFs, cofactors and core promoters and reveal general principles by which they influence transcription.  Overall design: GFP negative cells were isolated on D5, D6 and D7 after guide infection to identify the genes required for the activity of nine different transcription factors.. Sufficient cells were used to maintain 1000-fold representation at all stages of the screening process. The cells were transduced with an appropriate volume of viral supernatant to ensure only a single guide was present in most cells (MOI=0.3). At day 5, 6 and 7 day after guide infection, guide positive, GFP negative cells (< 25% of the MFI of the entire population) were sorted. Guide positive cells were also sorted as a library control at each time point to provide a library control reference to calculate enrichment. Four of the Gal4-TAD cell lines from the comparative CRISPR screens were also maintained until day 14 after guide infection to test for genes required for cell growth. These four TAD lines were used as independent replicates for the dropout analysis. Genomic DNA was extracted using Monarch? Genomic DNA purification kit (New England Biolabs), according to the manufacturer's instructions. PCR was conducted to maintain guide representation, using Q5? High Fidelity DNA Polymerase (New England Biolabs). PCR products were pooled and sequenced on the NextSeq500 using 75bp paired-end chemistry.