Nucleosome distortion as a possible mechanism of transcription activation domain function

Commonly asked questions about transcriptional activation domains

Eukaryotic transcription factors activate gene expression with their DNA-binding domains and activation domains. DNA-binding domains bind the genome by recognizing structurally related DNA sequences; they are structured, conserved, and predictable from protein sequences. Activation domains recruit chromatin modifiers, coactivator complexes, or basal transcriptional machinery via structurally diverse protein-protein interactions. Activation domains and DNA-binding domains have been called independent, modular units, but there are many departures from modularity, including interactions between these regions and overlap in function. Compared to DNA-binding domains, activation domains are poorly understood because they are poorly conserved, intrinsically disordered, and difficult to predict from protein sequences. This review, organized around commonly asked questions, describes recent progress that the field has made in understanding the sequence features that control activation domains and predicting them from sequence.

Systematic identification and characterization of repressive domains in Drosophila transcription factors

All multicellular life relies on differential gene expression, determined by regulatory DNA elements and DNA-binding transcription factors that mediate activation and repression via cofactor recruitment. While activators have been extensively characterized, repressors are less well studied: the identities and properties of their repressive domains (RDs) are typically unknown and the specific co-repressors (CoRs) they recruit have not been determined. Here, we develop a high-throughput, next-generation sequencing-based screening method, repressive-domain (RD)-seq, to systematically identify RDs in complex DNA-fragment libraries. Screening more than 200,000 fragments covering the coding sequences of all transcription-related proteins in Drosophila melanogaster, we identify 195 RDs in known repressors and in proteins not previously associated with repression. Many RDs contain recurrent short peptide motifs, which are conserved between fly and human and are required for RD function, as demonstrated by motif mutagenesis. Moreover, we show that RDs that contain one of five distinct repressive motifs interact with and depend on different CoRs, such as Groucho, CtBP, Sin3A, or Smrter. These findings advance our understanding of repressors, their sequences, and the functional impact of sequence-altering mutations and should provide a valuable resource for further studies.

43rd International Asilomar Chromatin, Chromosomes, and Epigenetics Conference

The 43rd Asilomar Chromatin, Chromosomes, and Epigenetics Conference was held in an entirely online format from December 9-11, 2021. The conference enabled presenters at various career stages to share promising new findings, and presentations covered modern chromatin research across an array of model systems. Topics ranged from the fundamental principles of nuclear organization and transcription regulation to key mechanisms underlying human disease. The meeting featured five keynote speakers from diverse backgrounds and was organized by: Juan Ausió, University of Victoria (British Columbia, Canada), James Davie, University of Manitoba (Manitoba, Canada), Philippe T. Georgel, Marshall University (West Virginia, USA), Michael Goldman, San Francisco State University (California, USA), LeAnn Howe, University of British Columbia (British Columbia, Canada), Jennifer A. Mitchell, University of Toronto (Ontario, Canada), and Sally G. Pasion, San Francisco State University (California, USA).

αα-hub coregulator structure and flexibility determine transcription factor binding and selection in regulatory interactomes

Formation of transcription factor (TF)-coregulator complexes is a key step in transcriptional regulation, with coregulators having essential functions as hub nodes in molecular networks. How specificity and selectivity are maintained in these nodes remain open questions. In this work, we addressed specificity in transcriptional networks using complexes formed between TFs and αα-hubs, which are defined by a common αα-hairpin secondary structure motif, as a model. Using NMR spectroscopy and binding thermodynamics, we analyzed the structure, dynamics, stability, and ligand-binding properties of the Arabidopsis thaliana RST domains from TAF4 and known binding partner RCD1, and the TAFH domain from human TAF4, allowing comparison across species, functions, and architectural contexts. While these αα-hubs shared the αα-hairpin motif, they differed in length and orientation of accessory helices as well as in their thermodynamic profiles of ligand binding. Whereas biologically relevant RCD1-ligand pairs displayed high affinity driven by enthalpy, TAF4-ligand interactions were entropy driven and exhibited less binding-induced structuring. We in addition identified a thermal unfolding state with a structured core for all three domains, although the temperature sensitivity differed. Thermal stability studies suggested that initial unfolding of the RCD1-RST domain localized around helix 1, lending this region structural malleability, while effects in TAF4-RST were more stochastic, suggesting variability in structural adaptability upon binding. Collectively, our results support a model in which hub structure, flexibility, and binding thermodynamics contribute to αα-hub-TF binding specificity, a finding of general relevance to the understanding of coregulator-ligand interactions and interactome sizes.

Activation of gene expression by detergent-like protein domains

The mechanisms by which transcriptional activation domains (tADs) initiate eukaryotic gene expression have been an enigma for decades because most tADs lack specificity in sequence, structure, and interactions with targets. Machine learning analysis of data sets of tAD sequences generated in vivo elucidated several functionality rules: the functional tAD sequences should (i) be devoid of or depleted with basic amino acid residues, (ii) be enriched with aromatic and acidic residues, (iii) be with aromatic residues localized mostly near the terminus of the sequence, and acidic residues localized more internally within a span of 20-30 amino acids, (iv) be with both aromatic and acidic residues preferably spread out in the sequence and not clustered, and (v) not be separated by occasional basic residues. These and other more subtle rules are not absolute, reflecting absence of a tAD consensus sequence, enormous variability, and consistent with surfactant-like tAD biochemical properties. The findings are compatible with the paradigm-shifting nucleosome detergent mechanism of gene expression activation, contributing to the development of the liquid-liquid phase separation model and the biochemistry of near-stochastic functional allosteric interactions.

Mediator subunit Med15 dictates the conserved "fuzzy" binding mechanism of yeast transcription activators Gal4 and Gcn4

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.

A High-Throughput Screen for Transcription Activation Domains Reveals Their Sequence Features and Permits Prediction by Deep Learning

Acidic transcription activation domains (ADs) are encoded by a wide range of seemingly unrelated amino acid sequences, making it difficult to recognize features that promote their dynamic behavior, "fuzzy" interactions, and target specificity. We screened a large set of random 30-mer peptides for AD function in yeast and trained a deep neural network (ADpred) on the AD-positive and -negative sequences. ADpred identifies known acidic ADs within transcription factors and accurately predicts the consequences of mutations. Our work reveals that strong acidic ADs contain multiple clusters of hydrophobic residues near acidic side chains, explaining why ADs often have a biased amino acid composition. ADs likely use a binding mechanism similar to avidity where a minimum number of weak dynamic interactions are required between activator and target to generate biologically relevant affinity and in vivo function. This mechanism explains the basis for fuzzy binding observed between acidic ADs and targets.

Order Through Disorder: The Characteristic Variability of Systems

Randomness characterizes many processes in nature, and therefore its importance cannot be overstated. In the present study, we investigate examples of randomness found in various fields, to underlie its fundamental processes. The fields we address include physics, chemistry, biology (biological systems from genes to whole organs), medicine, and environmental science. Through the chosen examples, we explore the seemingly paradoxical nature of life and demonstrate that randomness is preferred under specific conditions. Furthermore, under certain conditions, promoting or making use of variability-associated parameters may be necessary for improving the function of processes and systems.

A DNA methylation reader complex that enhances gene transcription

DNA methylation generally functions as a repressive transcriptional signal, but it is also known to activate gene expression. In either case, the downstream factors remain largely unknown. By using comparative interactomics, we isolated proteins in Arabidopsis thaliana that associate with methylated DNA. Two SU(VAR)3-9 homologs, the transcriptional antisilencing factor SUVH1, and SUVH3, were among the methyl reader candidates. SUVH1 and SUVH3 bound methylated DNA in vitro, were associated with euchromatic methylation in vivo, and formed a complex with two DNAJ domain-containing homologs, DNAJ1 and DNAJ2. Ectopic recruitment of DNAJ1 enhanced gene transcription in plants, yeast, and mammals. Thus, the SUVH proteins bind to methylated DNA and recruit the DNAJ proteins to enhance proximal gene expression, thereby counteracting the repressive effects of transposon insertion near genes.

'Nonlinear' Biochemistry of Nucleosome Detergents

The transcriptional activation domains (TADs) are critical for life, yet intrinsically disordered polypeptides with no specific consensus sequence, interacting with multiple targets via low-specificity fuzzy contacts. The recent integration of machine learning approaches in biochemistry allows analysis of large experimental datasets of functional TADs as a whole and clear observation of TAD features. The emerging picture describes TADs as sequences without consensus but with a variety of detergent-like mini-motifs enriched in negatively charged and aromatic amino acids. Comparison of the canonical direct coactivator recruitment model and a new model describing TADs as nucleosome detergents that trigger chromatin remodeling during gene activation helps solve a fundamental enigma of molecular biology spanning 30 years.

A high-throughput method to identify trans-activation domains within transcription factor sequences

Even though transcription factors (TFs) are central players of gene regulation and have been extensively studied, their regulatory trans-activation domains (tADs) often remain unknown and a systematic functional characterization of tADs is lacking. Here, we present a novel high-throughput approach tAD-seq to functionally test thousands of candidate tADs from different TFs in parallel. The tADs we identify by pooled screening validate in individual luciferase assays, whereas neutral regions do not. Interestingly, the tADs are found at arbitrary positions within the TF sequences and can contain amino acid (e.g., glutamine) repeat regions or overlap structured domains, including helix-loop-helix domains that are typically annotated as DNA-binding. We also identified tADs in the non-native reading frames, confirming that random sequences can function as tADs, albeit weakly. The identification of tADs as short protein sequences sufficient for transcription activation will enable the systematic study of TF function, which-particularly for TFs of different transcription activating functionalities-is still poorly understood.

High-throughput discovery of functional disordered regions: investigation of transactivation domains

Over 40% of proteins in any eukaryotic genome encode intrinsically disordered regions (IDRs) that do not adopt defined tertiary structures. Certain IDRs perform critical functions, but discovering them is non‐trivial as the biological context determines their function. We present IDR‐Screen, a framework to discover functional IDRs in a high‐throughput manner by simultaneously assaying large numbers of DNA sequences that code for short disordered sequences. Functionality‐conferring patterns in their protein sequence are inferred through statistical learning. Using yeast HSF1 transcription factor‐based assay, we discovered IDRs that function as transactivation domains (TADs) by screening a random sequence library and a designed library consisting of variants of 13 diverse TADs. Using machine learning, we find that segments devoid of positively charged residues but with redundant short sequence patterns of negatively charged and aromatic residues are a generic feature for TAD functionality. We anticipate that investigating defined sequence libraries using IDR‐Screen for specific functions can facilitate discovering novel and functional regions of the disordered proteome as well as understand the impact of natural and disease variants in disordered segments.

Transcription Activation Domains of the Yeast Factors Met4 and Ino2: Tandem Activation Domains with Properties Similar to the Yeast Gcn4 Activator

Eukaryotic transcription activation domains (ADs) are intrinsically disordered polypeptides that typically interact with coactivator complexes, leading to stimulation of transcription initiation, elongation, and chromatin modifications. Here we examined the properties of two strong and conserved yeast ADs: Met4 and Ino2. Both factors have tandem ADs that were identified by conserved sequence and functional studies. While the AD function of both factors depended on hydrophobic residues, Ino2 further required key conserved acidic and polar residues for optimal function. Binding studies showed that the ADs bound multiple Med15 activator-binding domains (ABDs) with similar orders of micromolar affinity and similar but distinct thermodynamic properties. Protein cross-linking data show that no unique complex was formed upon Met4-Med15 binding. Rather, we observed heterogeneous AD-ABD contacts with nearly every possible AD-ABD combination. Many of these properties are similar to those observed with yeast activator Gcn4, which forms a large heterogeneous, dynamic, and fuzzy complex with Med15. We suggest that this molecular behavior is common among eukaryotic activators.

Combinatorial function of transcription factors and cofactors

Differential gene expression gives rise to the many cell types of complex organisms. Enhancers regulate transcription by binding transcription factors (TFs), which in turn recruit cofactors to activate RNA Polymerase II at core promoters. Transcriptional regulation is typically mediated by distinct combinations of TFs, enabling a relatively small number of TFs to generate a large diversity of cell types. However, how TFs achieve combinatorial enhancer control and how enhancers, enhancer-bound TFs, and the cofactors they recruit regulate RNA Polymerase II activity is not entirely clear. Here, we review how TF synergy is mediated at the level of DNA binding and after binding, the role of cofactors and the post-translational modifications they catalyze, and discuss different models of enhancer-core-promoter communication.

The 9aaTAD Is Exclusive Activation Domain in Gal4

The Gal4 protein is a well-known prototypic acidic activator that has multiple activation domains. We have previously identified a new activation domain called the nine amino acid transactivation domain (9aaTAD) in Gal4 protein. The family of the 9aaTAD activators currently comprises over 40 members including p53, MLL, E2A and other members of the Gal4 family; Oaf1, Pip2, Pdr1 and Pdr3. In this study, we revised function of all reported Gal4 activation domains. Surprisingly, we found that beside of the activation domain 9aaTAD none of the previously reported activation domains had considerable transactivation potential and were not involved in the activation of transcription. Our results demonstrated that the 9aaTAD domain is the only decisive activation domain in the Gal4 protein. We found that the artificial peptides included in the original Gal4 constructs were results of an unintended consequence of cloning that were responsible for the artificial transcriptional activity. Importantly, the activation domain 9aaTAD, which is the exclusive activation domain in Gal4, is also the central part of a conserved sequence recognized by the inhibitory protein Gal80. We propose a revision of the Gal4 regulation, in which the activation domain 9aaTAD is directly linked to both activation function and Gal80 mediated inhibition.