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Three general classes of yeast protein-coding genes are distinguished by their dependence on the transcription cofactors TFIID, SAGA and Mediator (MED) Tail, but little is known about whether this dependence is determined by the core promoter, Upstream activation sites (UASs), or other gene features. It is also unclear whether UASs can broadly activate transcription from the different promoter classes or whether efficient transcription requires matching UASs and promoters of similar gene class. Here we measure transcription and cofactor specificity for tens of thousands of UAS-core promoter combinations. We find that

Despite decades of research, characterization of the effects of polymer chain dispersity on the structural properties of block copolymer thin films remains challenging. We present an integrated exp...

Mediator (MED) is a conserved factor with important roles in both basal and activated transcription. It is believed that MED plays a direct role in transcriptional regulation at most genes by functionally bridging enhancers and promoters. Here, we investigate the genome-wide roles of yeast MED by rapid depletion of its activator-binding domain (Tail) and monitoring changes in nascent transcription. We find that MED Tail and activator-mediated MED recruitment regulate only a small subset of genes. At most genes, MED bypasses the UAS and is directly recruited to promoters to facilitate transcription initiation. Our results define three classes of genes that differ in PIC assembly pathways and the requirements for MED Tail, SAGA, TFIID and BET factors Bdf1/2. We also find that the depletion of the MED middle module subunit Med7 mimics inactivation of Tail, suggesting a functional link. Our combined results have broad implications for the roles of MED, other coactivators, and mechanisms of transcriptional regulation at different gene classes.

Mediator (MED) is a conserved factor with important roles in basal and activated transcription. Here, we investigate the genome-wide roles of yeast MED by rapid depletion of its activator-binding domain (Tail) and monitoring changes in nascent transcription. Rapid Tail depletion surprisingly reduces transcription from only a small subset of genes. At most of these Tail-dependent genes, in unperturbed conditions, MED is detected at both the UASs and promoters. In contrast, at most Tail-independent genes, we find MED primarily at promoters but not at the UASs. These results suggest that MED Tail and activator-mediated MED recruitment regulates only a small subset of genes. Furthermore, we define three classes of genes that differ in PIC assembly pathways and the requirements for MED Tail, SAGA, TFIID, and BET factors Bdf1/2. Our combined results have broad implications for the roles of MED, other coactivators, and mechanisms of transcriptional regulation at different gene classes.

Mittal and colleagues have raised questions about mapping transcription factor locations on DNA using the MNase-based ChEC-seq method (Mittal et al., 2021). Partly due to this concern, we modified the experimental conditions of the MNase cleavage step and subsequent computational analyses, resulting in more stringent conditions for mapping protein-DNA interactions (Donczew et al., 2020). The revised method (dx.doi.org/10.17504/protocols.io.bizgkf3w) answers questions raised by Mittal et al. and, without changing earlier conclusions, identified widespread promoter binding of the transcription coactivators TFIID and SAGA at active genes. The revised method is also suitable for accurately mapping the genome-wide locations of DNA sequence-specific transcription factors.

The acidic activation domain (AD) of yeast transcription factor Gal4 plays a dual role in transcription repression and activation through binding to Gal80 repressor and Mediator subunit Med15. The activation function of Gal4 arises from two hydrophobic regions within the 40-residue AD. We show by NMR that each AD region binds the Mediator subunit Med15 using a “fuzzy” protein interface. Remarkably, comparison of chemical shift perturbations shows that Gal4 and Gcn4, two intrinsically disordered ADs of different sequence, interact nearly identically with Med15. The finding that two ADs of different sequence use an identical fuzzy binding mechanism shows a common sequence-independent mechanism for AD-Mediator binding, similar to interactions within a hydrophobic cloud. In contrast, the same region of Gal4 AD interacts strongly with Gal80 via a distinct structured complex, implying that the structured binding partner of an intrinsically disordered protein dictates the type of protein–protein interaction., The intrinsically disordered acidic activation domain (AD) of the yeast transcription factor Gal4 acts through binding to the Med15 subunit of the Mediator complex. Here, the authors show that Gal4 interacts with Med15 through an identical fuzzy binding mechanism as Gcn4 AD, which has a different sequence, revealing a common sequence-independent mechanism for AD-Mediator binding. In contrast, Gal4 AD binds to the Gal80 repressor as a structured polypeptide, which strongly suggests that the structured binding partner dictates the type of protein–protein interaction for an intrinsically disordered protein.

Oak Ridge National Laboratory (ORNL) experimental neutron science facilities produce 1.2\,TB a day of raw event-based data that is stored using the standard metadata-rich NeXus schema built on top of the HDF5 file format. Performance of several data reduction workflows is largely determined by the amount of time spent on the loading and processing algorithms in Mantid, an open-source data analysis framework used across several neutron sciences facilities around the world. The present work introduces new data management algorithms to address identified input output (I/O) bottlenecks on Mantid. First, we introduce an in-memory binary-tree metadata index that resemble NeXus data access patterns to provide a scalable search and extraction mechanism. Second, data encapsulation in Mantid algorithms is optimally redesigned to reduce the total compute and memory runtime footprint associated with metadata I/O reconstruction tasks. Results from this work show speed ups in wall-clock time on ORNL data reduction workflows, ranging from 11\% to 30\% depending on the complexity of the targeted instrument-specific data. Nevertheless, we highlight the need for more research to address reduction challenges as experimental data volumes increase., Comment: 7 pages, 4 figures, International Workshop on Big Data Reduction held with 2020 IEEE International Conference on Big Data

ChEC-seq and other nuclease-based methods such as Cut&Run map protein locations on DNA by targeting nuclease activity to specific transcription factors and mapping the resulting DNA cleavages (Schmid et al. 2004; Skene and Henikoff 2017; Zentner et al. 2015). For ChEC-seq, yeast cells expressing a protein-micrococcal nuclease (MNase) fusion are permeabilized, MNase is activated by the addition of calcium, and the resulting DNA fragments are mapped. Potential advantages of this approach include avoiding non-specific protein-DNA crosslinking in highly transcribed regions, efficient mapping of factors that do not directly bind DNA and, more sensitive detection of protein-DNA interactions. We optimized the original ChEC-seq protocol to minimize non-specific DNA cleavage, avoid over digestion at authentic binding sites, and efficiently assay large numbers of factors. We also created a robust data analysis pipeline that incorporates peak calling to map binding sites and quantitative analysis, based on utilization of spike-in DNA, to compare factor-DNA binding under different conditions. We used this modified approach to map genome-wide distributions of the transcription coactivators TFIID and SAGA (Donczew et al. 2020) as well as transcription factors Abf1 and Rap1 (Donczew et al, submitted to Mol Cell). References Donczew R, Warfield L, Pacheco D, Erijman A, Hahn S. 2020. Two roles for the yeast transcription coactivator SAGA and a set of genes redundantly regulated by TFIID and SAGA.eLife 9: e50109. Schmid M, Durussel T, Laemmli UK. 2004. ChIC and ChEC; genomic mapping of chromatin proteins.Mol Cell 16: 147–157. Skene PJ, Henikoff S. 2017. An efficient targeted nuclease strategy for high-resolution mapping of DNA binding sites.eLife 6: e21856. Warfield L, Ramachandran S, Baptista T, Devys D, Tora L, Hahn S. 2017. Transcription of nearly all yeast RNA Polymerase II-transcribed genes is dependent on transcription factor TFIID.Mol Cell. 68:118-129 Zentner GE, Kasinathan S, Xin B, Rohs R, Henikoff S. 2015. ChEC-seq kinetics discriminates transcription factor binding sites by DNA sequence and shape in vivo.Nat Commun 6: 8733.

This chapter counters the tendency of many comparative and transnational studies of the United States to focus on the Atlantic and ties between Europe and the Americas. The author reorients the reader’s gaze west and southward in the Civil War era to argue for the simultaneity of nation state and empire as governing forms and ideological goals. He highlights the sometimes seemingly contradictory impulses involved in the consolidation of sovereignty within U.S. borders and also in the contemporaneous movement outward to conquer and subjugate new lands and peoples.

Summary Transcription activation domains (ADs) are inherently disordered proteins that often target multiple coactivator complexes, but the specificity of these interactions is not understood. Efficient transcription activation by yeast Gcn4 requires its tandem ADs and four activator-binding domains (ABDs) on its target, the Mediator subunit Med15. Multiple ABDs are a common feature of coactivator complexes. We find that the large Gcn4-Med15 complex is heterogeneous and contains nearly all possible AD-ABD interactions. Gcn4-Med15 forms via a dynamic fuzzy protein-protein interface, where ADs bind the ABDs in multiple orientations via hydrophobic regions that gain helicity. This combinatorial mechanism allows individual low-affinity and specificity interactions to generate a biologically functional, specific, and higher affinity complex despite lacking a defined protein-protein interface. This binding strategy is likely representative of many activators that target multiple coactivators, as it allows great flexibility in combinations of activators that can cooperate to regulate genes with variable coactivator requirements., Graphical abstract In Brief: Tuttle et al. report a “fuzzy free-for-all” interaction mechanism that explains how seemingly unrelated transcription activators converge on a limited number of coactivator targets. The mechanism provides a rationale for the observation that individually weak and low-specificity interactions can combine to produce biologically critical function without requiring highly ordered structure.

The general transcription factor TFIIH contains three ATP-dependent catalytic activities. TFIIH functions in nucleotide excision repair primarily as a DNA helicase and in Pol II transcription initiation as a dsDNA translocase and protein kinase. During initiation, the XPB/Ssl2 subunit of TFIIH couples ATP hydrolysis to dsDNA translocation facilitating promoter opening and the kinase module phosphorylates Pol II to facilitate the transition to elongation. These functions are conserved between metazoans and yeast; however, yeast TFIIH also drives transcription start-site scanning in which Pol II scans downstream DNA to locate productive start-sites. The ten-subunit holo-TFIIH from S. cerevisiae has a processive dsDNA translocase activity required for scanning and a structural role in scanning has been ascribed to the three-subunit TFIIH kinase module. Here, we assess the dsDNA translocase activity of ten-subunit holo- and core-TFIIH complexes (i.e. seven subunits, lacking the kinase module) from both S. cerevisiae and H. sapiens. We find that neither holo nor core human TFIIH exhibit processive translocation, consistent with the lack of start-site scanning in humans. Furthermore, in contrast to holo-TFIIH, the S. cerevisiae core-TFIIH also lacks processive translocation and its dsDNA-stimulated ATPase activity was reduced ~5-fold to a level comparable to the human complexes, potentially explaining the reported upstream shift in start-site observed in vitro in the absence of the S. cerevisiae kinase module. These results suggest that neither human nor S. cerevisiae core-TFIIH can translocate efficiently, and that the S. cerevisiae kinase module functions as a processivity factor to allow for robust transcription start-site scanning.

Gene expression is controlled by transcription factors (TFs) that consist of DNA-binding domains (DBDs) and activation domains (ADs). The DBDs have been well-characterized, but little is known about the mechanisms by which ADs effect gene activation. Here we report that diverse ADs form phase-separated condensates with the Mediator coactivator. For the OCT4 and GCN4 TFs, we show that the ability to form phase-separated droplets with Mediator in vitro and the ability to activate genes in vivo are dependent on the same amino acid residues. For the estrogen receptor (ER), a ligand-dependent activator, we show that estrogen enhances phase separation with Mediator, again linking phase separation with gene activation. These results suggest that diverse TFs can interact with Mediator through the phase-separating capacity of their ADs and that formation of condensates with Mediator is involved in gene activation.

Transcription activation domains (ADs) are encoded by a wide range of seemingly unrelated amino acid sequences, making it difficult to recognize features that permit their dynamic behavior, fuzzy interactions and target specificity. We screened a large set of random 30-mer peptides for AD function and trained a deep neural network (ADpred) on the AD-positive and negative sequences. ADpred correctly identifies known ADs within protein sequences and accurately predicts the consequences of mutations. We show that functional ADs are (1) located within intrinsically disordered regions with biased amino acid composition, (2) contain clusters of hydrophobic residues near acidic side chains, (3) are enriched or depleted for particular dipeptide sequences, and (4) have higher helical propensity than surrounding regions. Taken together, our findings fit the model of “fuzzy” binding through hydrophobic protein-protein interfaces, where activator-coactivator binding takes place in a dynamic hydrophobic environment rather than through combinations of sequence-specific interactions.