'Nonlinear' Biochemistry of Nucleosome Detergents

Systematic identification of transcriptional activation domains from non-transcription factor proteins in plants and yeast

Transcription factors can promote gene expression through activation domains. Whole-genome screens have systematically mapped activation domains in transcription factors but not in non-transcription factor proteins (e.g., chromatin regulators and coactivators). To fill this knowledge gap, we employed the activation domain predictor PADDLE to analyze the proteomes of Arabidopsis thaliana and Saccharomyces cerevisiae. We screened 18,000 predicted activation domains from >800 non-transcription factor genes in both species, confirming that 89% of candidate proteins contain active fragments. Our work enables the annotation of hundreds of nuclear proteins as putative coactivators, many of which have never been ascribed any function in plants. Analysis of peptide sequence compositions reveals how the distribution of key amino acids dictates activity. Finally, we validated short, "universal" activation domains with comparable performance to state-of-the-art activation domains used for genome engineering. Our approach enables the genome-wide discovery and annotation of activation domains that can function across diverse eukaryotes.

Predicting transcriptional activation domain function using Graph Neural Networks

Analysis of factors that lead to the functionality of transcriptional activation domains remains a crucial and yet challenging task owing to the significant diversity in their sequences and their intrinsically disordered nature. Almost all existing methods that have aimed to predict activation domains have involved traditional machine learning approaches, such as logistic regression, that are unable to capture complex patterns in data or plain convolutional neural networks and have been limited in exploration of structural features. However, there is a tremendous potential in the inspection of the structural properties of activation domains, and an opportunity to investigate complex relationships between features of residues in the sequence. To address these, we have utilized the power of graph neural networks which can represent structural data in the form of nodes and edges, allowing nodes to exchange information among themselves. We have experimented with two kinds of graph formulations, one involving residues as nodes and the other assigning atoms to be the nodes. A logistic regression model was also developed to analyze feature importance. For all the models, several feature combinations were experimented with. The residue-level GNN model with amino acid type, residue position, acidic/basic/aromatic property and secondary structure feature combination gave the best performing model with accuracy, F1 score and AUROC of 97.9%, 71% and 97.1% respectively which outperformed other existing methods in the literature when applied on the dataset we used. Among the other structure-based features that were analyzed, the amphipathic property of helices also proved to be an important feature for classification. Logistic regression results showed that the most dominant feature that makes a sequence functional is the frequency of different types of amino acids in the sequence. Our results consistent have shown that functional sequences have more acidic and aromatic residues whereas basic residues are seen more in non-functional sequences.

Systematic identification of transcriptional activator domains from non-transcription factor proteins in plants and yeast

Transcription factors promote gene expression via trans-regulatory activation domains. Although whole genome scale screens in model organisms (e.g. human, yeast, fly) have helped identify activation domains from transcription factors, such screens have been less extensively used to explore the occurrence of activation domains in non-transcription factor proteins, such as transcriptional coactivators, chromatin regulators and some cytosolic proteins, leaving a blind spot on what role activation domains in these proteins could play in regulating transcription. We utilized the activation domain predictor PADDLE to mine the entire proteomes of two model eukaryotes, Arabidopsis thaliana and Saccharomyces cerevisiae ( 1 ). We characterized 18,000 fragments covering predicted activation domains from >800 non-transcription factor genes in both species, and experimentally validated that 89% of proteins contained fragments capable of activating transcription in yeast. Peptides with similar sequence composition show a broad range of activities, which is explained by the arrangement of key amino acids. We also annotated hundreds of nuclear proteins with activation domains as putative coactivators; many of which have never been ascribed any function in plants. Furthermore, our library contains >250 non-nuclear proteins containing peptides with activation domain function across both eukaryotic lineages, suggesting that there are unknown biological roles of these peptides beyond transcription. Finally, we identify and validate short, 'universal' eukaryotic activation domains that activate transcription in both yeast and plants with comparable or stronger performance to state-of-the-art activation domains. Overall, our dual host screen provides a blueprint on how to systematically discover novel genetic parts for synthetic biology that function across a wide diversity of eukaryotes.

Significance Statement

Activation domains promote transcription and play a critical role in regulating gene expression. Although the mapping of activation domains from transcription factors has been carried out in previous genome-wide screens, their occurrence in non-transcription factors has been less explored. We utilize an activation domain predictor to mine the entire proteomes of Arabidopsis thaliana and Saccharomyces cerevisiae for new activation domains on non-transcription factor proteins. We validate peptides derived from >750 non-transcription factor proteins capable of activating transcription, discovering many potentially new coactivators in plants. Importantly, we identify novel genetic parts that can function across both species, representing unique synthetic biology tools.

Nucleolar- and Nuclear-Stress-Induced Membrane-Less Organelles: A Proteome Analysis through the Prism of Liquid-Liquid Phase Separation

Radical changes in the idea of the organization of intracellular space that occurred in the early 2010s made it possible to consider the formation and functioning of so-called membrane-less organelles (MLOs) based on a single physical principle: the liquid-liquid phase separation (LLPS) of biopolymers. Weak non-specific inter- and intramolecular interactions of disordered polymers, primarily intrinsically disordered proteins, and RNA, play a central role in the initiation and regulation of these processes. On the other hand, in some cases, the "maturation" of MLOs can be accompanied by a "liquid-gel" phase transition, where other types of interactions can play a significant role in the reorganization of their structure. In this work, we conducted a bioinformatics analysis of the propensity of the proteomes of two membrane-less organelles, formed in response to stress in the same compartment, for spontaneous phase separation and examined their intrinsic disorder predispositions. These MLOs, amyloid bodies (A-bodies) formed in the response to acidosis and heat shock and nuclear stress bodies (nSBs), are characterized by a partially overlapping composition, but show different functional activities and morphologies. We show that the proteomes of these biocondensates are differently enriched in proteins, and many have high potential for spontaneous LLPS that correlates with the different morphology and function of these organelles. The results of these analyses allowed us to evaluate the role of weak interactions in the formation and functioning of these important organelles.

Cellular proteins act as surfactants to control the interfacial behavior and function of biological condensates

Interfacial tension governs the behaviors and physiological functions of multiple biological condensates during diverse biological processes. Little is known about whether there are cellular surfactant factors that regulate the interfacial tension and functions of biological condensates within physiological environments. TFEB, a master transcription factor that controls expression of autophagic-lysosomal genes, assembles into transcriptional condensates to control the autophagy-lysosome pathway (ALP). Here, we show that interfacial tension modulates the transcriptional activity of TFEB condensates. MLX, MYC, and IPMK act as synergistic surfactants to decrease the interfacial tension and consequent DNA affinity of TFEB condensates. The interfacial tension of TFEB condensates is quantitatively correlated to their DNA affinity and subsequent ALP activity. The interfacial tension and DNA affinity of condensates formed by TAZ-TEAD4 are also regulated by the synergistic surfactant proteins RUNX3 and HOXA4. Our results indicate that the interfacial tension and functions of biological condensates can be controlled by cellular surfactant proteins in human cells.

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).

Can aggressive cancers be identified by the "aggressiveness" of their chromatin?

Phenotypic plasticity is a crucial feature of aggressive cancer, providing the means for cancer progression. Stochastic changes in tumor cell transcriptional programs increase the chances of survival under any condition. I hypothesize that unstable chromatin permits stochastic transitions between transcriptional programs in aggressive cancers and supports non-genetic heterogeneity of tumor cells as a basis for their adaptability. I present a mechanistic model for unstable chromatin which includes destabilized nucleosomes, mobile chromatin fibers and random enhancer-promoter contacts, resulting in stochastic transcription. I suggest potential markers for "unsettled" chromatin in tumors associated with poor prognosis. Although many of the characteristics of unstable chromatin have been described, they were mostly used to explain changes in the transcription of individual genes. I discuss approaches to evaluate the role of unstable chromatin in non-genetic tumor cell heterogeneity and suggest using the degree of chromatin instability and transcriptional noise in tumor cells to predict cancer aggressiveness.

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.

On the potential of machine learning to examine the relationship between sequence, structure, dynamics and function of intrinsically disordered proteins

Intrinsically disordered proteins (IDPs) constitute a broad set of proteins with few uniting and many diverging properties. IDPs-and intrinsically disordered regions (IDRs) interspersed between folded domains-are generally characterized as having no persistent tertiary structure; instead they interconvert between a large number of different and often expanded structures. IDPs and IDRs are involved in an enormously wide range of biological functions and reveal novel mechanisms of interactions, and while they defy the common structure-function paradigm of folded proteins, their structural preferences and dynamics are important for their function. We here discuss open questions in the field of IDPs and IDRs, focusing on areas where machine learning and other computational methods play a role. We discuss computational methods aimed to predict transiently formed local and long-range structure, including methods for integrative structural biology. We discuss the many different ways in which IDPs and IDRs can bind to other molecules, both via short linear motifs, as well as in the formation of larger dynamic complexes such as biomolecular condensates. We discuss how experiments are providing insight into such complexes and may enable more accurate predictions. Finally, we discuss the role of IDPs in disease and how new methods are needed to interpret the mechanistic effects of genomic variants in IDPs.

IDDomainSpotter: Compositional bias reveals domains in long disordered protein regions-Insights from transcription factors

Protein domains constitute regions of distinct structural properties and molecular functions that are retained when removed from the rest of the protein. However, due to the lack of tertiary structure, the identification of domains has been largely neglected for long (>50 residues) intrinsically disordered regions. Here we present a sequence-based approach to assess and visualize domain organization in long intrinsically disordered regions based on compositional sequence biases. An online tool to find putative intrinsically disordered domains (IDDomainSpotter) in any protein sequence or sequence alignment using any particular sequence trait is available at http://www.bio.ku.dk/sbinlab/IDDomainSpotter. Using this tool, we have identified a putative domain enriched in hydrophilic and disorder-promoting residues (Pro, Ser, and Thr) and depleted in positive charges (Arg and Lys) bordering the folded DNA-binding domains of several transcription factors (p53, GCR, NAC46, MYB28, and MYB29). This domain, from two different MYB transcription factors, was characterized biophysically to determine its properties. Our analyses show the domain to be extended, dynamic and highly disordered. It connects the DNA-binding domain to other disordered domains and is present and conserved in several transcription factors from different families and domains of life. This example illustrates the potential of IDDomainSpotter to predict, from sequence alone, putative domains of functional interest in otherwise uncharacterized disordered proteins.

Evolutionary conservation of the intrinsic disorder-based Radical-Induced Cell Death1 hub interactome

Radical-Induced Cell Death1 (RCD1) functions as a cellular hub interacting with intrinsically disordered transcription factor regions, which lack a well-defined three-dimensional structure, to regulate plant stress. Here, we address the molecular evolution of the RCD1-interactome. Using bioinformatics, its history was traced back more than 480 million years to the emergence of land plants with the RCD1-binding short linear motif (SLiM) identified from mosses to flowering plants. SLiM variants were biophysically verified to be functional and to depend on the same RCD1 residues as the DREB2A transcription factor. Based on this, numerous additional members may be assigned to the RCD1-interactome. Conservation was further strengthened by similar intrinsic disorder profiles of the transcription factor homologs. The unique structural plasticity of the RCD1-interactome, with RCD1-binding induced α-helix formation in DREB2A, but not detectable in ANAC046 or ANAC013, is apparently conserved. Thermodynamic analysis also indicated conservation with interchangeability between Arabidopsis and soybean RCD1 and DREB2A, although with fine-tuned co-evolved binding interfaces. Interruption of conservation was observed, as moss DREB2 lacked the SLiM, likely reflecting differences in plant stress responses. This whole-interactome study uncovers principles of the evolution of SLiM:hub-interactions, such as conservation of α-helix propensities, which may be paradigmatic for disorder-based interactomes in eukaryotes.

Twenty years of Mediator complex structural studies

Mediator is a large multiprotein complex conserved in all eukaryotes that plays an essential role in transcriptional regulation. Mediator comprises 25 subunits in yeast and 30 subunits in humans that form three main modules and a separable four-subunit kinase module. For nearly 20 years, because of its size and complexity, Mediator has posed a formidable challenge to structural biologists. The first two-dimensional electron microscopy (EM) projection map of Mediator leading to the canonical view of its division in three topological modules named Head, Middle and Tail, was published in 1999. Within the last few years, optimization of Mediator purification combined with technical and methodological advances in cryo-electron microscopy (cryo-EM) have revealed unprecedented details of Mediator subunit organization, interactions with RNA polymerase II and parts of its core structure at high resolution. To celebrate the twentieth anniversary of the first Mediator EM reconstruction, we look back on the structural studies of Mediator complex from a historical perspective and discuss them in the light of our current understanding of its role in transcriptional regulation.