Context-specific functions of chromatin remodellers in development and disease.

Chromatin remodellers were once thought to be highly redundant and nonspecific in their actions. However, recent human genetic studies demonstrate remarkable biological specificity and dosage sensitivity of the thirty-two adenosine triphosphate (ATP)-dependent chromatin remodellers encoded in the human genome. Mutations in remodellers produce many human developmental disorders and cancers, motivating efforts to investigate their distinct functions in biologically relevant settings. Exquisitely specific biological functions seem to be an emergent property in mammals, and in many cases are based on the combinatorial assembly of subunits and the generation of stable, composite surfaces. Critical interactions between remodelling complex subunits, the nucleosome and other transcriptional regulators are now being defined from structural and biochemical studies. In addition, in vivo analyses of remodellers at relevant genetic loci have provided minute-by-minute insights into their dynamics. These studies are proposing new models for the determinants of remodeller localization and function on chromatin.

Rewiring cancer drivers to activate apoptosis.

Genes that drive the proliferation, survival, invasion and metastasis of malignant cells have been identified for many human cancers1-4. Independent studies have identified cell death pathways that eliminate cells for the good of the organism5,6. The coexistence of cell death pathways with driver mutations suggests that the cancer driver could be rewired to activate cell death using chemical inducers of proximity (CIPs). Here we describe a new class of molecules called transcriptional/epigenetic CIPs (TCIPs) that recruit the endogenous cancer driver, or a downstream transcription factor, to the promoters of cell death genes, thereby activating their expression. We focused on diffuse large B cell lymphoma, in which the transcription factor B cell lymphoma 6 (BCL6) is deregulated7. BCL6 binds to the promoters of cell death genes and epigenetically suppresses their expression8. We produced TCIPs by covalently linking small molecules that bind BCL6 to those that bind to transcriptional activators that contribute to the oncogenic program, such as BRD4. The most potent molecule, TCIP1, increases binding of BRD4 by 50% over genomic BCL6-binding sites to produce transcriptional elongation at pro-apoptotic target genes within 15 min, while reducing binding of BRD4 over enhancers by only 10%, reflecting a gain-of-function mechanism. TCIP1 kills diffuse large B cell lymphoma cell lines, including chemotherapy-resistant, TP53-mutant lines, at EC50 of 1-10 nM in 72 h and exhibits cell-specific and tissue-specific effects, capturing the combinatorial specificity inherent to transcription. The TCIP concept also has therapeutic applications in regulating the expression of genes for regenerative medicine and developmental disorders.

BAF subunit switching regulates chromatin accessibility to control cell cycle exit in the developing mammalian cortex.

mSWI/SNF or BAF chromatin regulatory complexes are dosage-sensitive regulators of human neural development frequently mutated in autism spectrum disorders and intellectual disability. Cell cycle exit and differentiation of neural stem/progenitor cells is accompanied by BAF subunit switching to generate neuron-specific nBAF complexes. We manipulated the timing of BAF subunit exchange in vivo and found that early loss of the npBAF subunit BAF53a stalls the cell cycle to disrupt neurogenesis. Loss of BAF53a results in decreased chromatin accessibility at specific neural transcription factor binding sites, including the pioneer factors SOX2 and ASCL1, due to Polycomb accumulation. This results in repression of cell cycle genes, thereby blocking cell cycle progression and differentiation. Cell cycle block upon Baf53a deletion could be rescued by premature expression of the nBAF subunit BAF53b but not by other major drivers of proliferation or differentiation. WNT, EGF, bFGF, SOX2, c-MYC, or PAX6 all fail to maintain proliferation in the absence of BAF53a, highlighting a novel mechanism underlying neural progenitor cell cycle exit in the continued presence of extrinsic proliferative cues.

CHD8 dosage regulates transcription in pluripotency and early murine neural differentiation.

The chromatin remodeler CHD8 is among the most frequently mutated genes in autism spectrum disorder (ASD). CHD8 has a dosage-sensitive role in ASD, but when and how it becomes critical to human social function is unclear. Here, we conducted genomic analyses of heterozygous and homozygous Chd8 mouse embryonic stem cells and differentiated neural progenitors. We identify dosage-sensitive CHD8 transcriptional targets, sites of regulated accessibility, and an unexpected cooperation with SOX transcription factors. Collectively, our findings reveal that CHD8 negatively regulates expression of neuronal genes to maintain pluripotency and also during differentiation. Thus, CHD8 is essential for both the maintenance of pluripotency and neural differentiation, providing mechanistic insight into its function with potential implications for ASD.

Loss of the neural-specific BAF subunit ACTL6B relieves repression of early response genes and causes recessive autism.

Synaptic activity in neurons leads to the rapid activation of genes involved in mammalian behavior. ATP-dependent chromatin remodelers such as the BAF complex contribute to these responses and are generally thought to activate transcription. However, the mechanisms keeping such "early activation" genes silent have been a mystery. In the course of investigating Mendelian recessive autism, we identified six families with segregating loss-of-function mutations in the neuronal BAF (nBAF) subunit ACTL6B (originally named BAF53b). Accordingly, ACTL6B was the most significantly mutated gene in the Simons Recessive Autism Cohort. At least 14 subunits of the nBAF complex are mutated in autism, collectively making it a major contributor to autism spectrum disorder (ASD). Patient mutations destabilized ACTL6B protein in neurons and rerouted dendrites to the wrong glomerulus in the fly olfactory system. Humans and mice lacking ACTL6B showed corpus callosum hypoplasia, indicating a conserved role for ACTL6B in facilitating neural connectivity. Actl6b knockout mice on two genetic backgrounds exhibited ASD-related behaviors, including social and memory impairments, repetitive behaviors, and hyperactivity. Surprisingly, mutation of Actl6b relieved repression of early response genes including AP1 transcription factors (Fos, Fosl2, Fosb, and Junb), increased chromatin accessibility at AP1 binding sites, and transcriptional changes in late response genes associated with early response transcription factor activity. ACTL6B loss is thus an important cause of recessive ASD, with impaired neuron-specific chromatin repression indicated as a potential mechanism.

RNA-Puzzles Round IV: 3D structure predictions of four ribozymes and two aptamers.

RNA-Puzzles is a collective endeavor dedicated to the advancement and improvement of RNA 3D structure prediction. With agreement from crystallographers, the RNA structures are predicted by various groups before the publication of the crystal structures. We now report the prediction of 3D structures for six RNA sequences: four nucleolytic ribozymes and two riboswitches. Systematic protocols for comparing models and crystal structures are described and analyzed. In these six puzzles, we discuss (i) the comparison between the automated web servers and human experts; (ii) the prediction of coaxial stacking; (iii) the prediction of structural details and ligand binding; (iv) the development of novel prediction methods; and (v) the potential improvements to be made. We show that correct prediction of coaxial stacking and tertiary contacts is essential for the prediction of RNA architecture, while ligand binding modes can only be predicted with low resolution and simultaneous prediction of RNA structure with accurate ligand binding still remains out of reach. All the predicted models are available for the future development of force field parameters and the improvement of comparison and assessment tools.

Alternative Splicing of Opioid Receptor Genes Shows a Conserved Pattern for 6TM Receptor Variants.

The opioid receptor (OPR) family comprises the mu-, delta-, and kappa-opioid, and nociceptin receptors that belong to the superfamily of 7-transmembrane spanning G protein-coupled receptors (GPCRs). The mu-opioid receptor is the main target for clinically used opioid analgesics, and its biology has been extensively studied. The N-terminally truncated 6TM receptors isoform produced through alternative splicing of the OPRM1 gene displays unique signaling and analgesic properties, but it is unclear if other OPRs have the same ability. In this study, we have built a comprehensive map of alternative splicing events that produce 6TM receptor variants in all the OPRs and demonstrated their evolutionary conservation. We then obtained evidence for their translation through ribosomal footprint analysis. We discovered that N-terminally truncated 6TM GPCRs are rare in the human genome and OPRs are overrepresented in this group. Finally, we also observed a significant enrichment of 6TM GPCR genes among genes associated with pain, psychiatric disorders, and addiction. Understanding the biology of 6TM receptors and leveraging this knowledge for drug development should pave the way for novel therapies.

Limits in accuracy and a strategy of RNA structure prediction using experimental information.

RNA structural complexity and flexibility present a challenge for computational modeling efforts. Experimental information and bioinformatics data can be used as restraints to improve the accuracy of RNA tertiary structure prediction. Regarding utilization of restraints, the fundamental questions are: (i) What is the limit in prediction accuracy that one can achieve with arbitrary number of restraints? (ii) Is there a strategy for selection of the minimal number of restraints that would result in the best structural model? We address the first question by testing the limits in prediction accuracy using native contacts as restraints. To address the second question, we develop an algorithm based on the distance variation allowed by secondary structure (DVASS), which ranks restraints according to their importance to RNA tertiary structure prediction. We find that due to kinetic traps, the greatest improvement in the structure prediction accuracy is achieved when we utilize only 40-60% of the total number of native contacts as restraints. When the restraints are sorted by DVASS algorithm, using only the first 20% ranked restraints can greatly improve the prediction accuracy. Our findings suggest that only a limited number of strategically selected distance restraints can significantly assist in RNA structure modeling.

Direct identification of base-paired RNA nucleotides by correlated chemical probing.

Many RNA molecules fold into complex secondary and tertiary structures that play critical roles in biological function. Among the best-established methods for examining RNA structure are chemical probing experiments, which can report on local nucleotide structure in a concise and extensible manner. While probing data are highly useful for inferring overall RNA secondary structure, these data do not directly measure through-space base-pairing interactions. We recently introduced an approach for single-molecule correlated chemical probing with dimethyl sulfate (DMS) that measures RNA interaction groups by mutational profiling (RING-MaP). RING-MaP experiments reveal diverse through-space interactions corresponding to both secondary and tertiary structure. Here we develop a framework for using RING-MaP data to directly and robustly identify canonical base pairs in RNA. When applied to three representative RNAs, this framework identified 20%-50% of accepted base pairs with a <10% false discovery rate, allowing detection of 88% of duplexes containing four or more base pairs, including pseudoknotted pairs. We further show that base pairs determined from RING-MaP analysis significantly improve secondary structure modeling. RING-MaP-based correlated chemical probing represents a direct, experimentally concise, and accurate approach for detection of individual base pairs and helices and should greatly facilitate structure modeling for complex RNAs.

RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme.

RNA-Puzzles is a collective experiment in blind 3D RNA structure prediction. We report here a third round of RNA-Puzzles. Five puzzles, 4, 8, 12, 13, 14, all structures of riboswitch aptamers and puzzle 7, a ribozyme structure, are included in this round of the experiment. The riboswitch structures include biological binding sites for small molecules (S-adenosyl methionine, cyclic diadenosine monophosphate, 5-amino 4-imidazole carboxamide riboside 5'-triphosphate, glutamine) and proteins (YbxF), and one set describes large conformational changes between ligand-free and ligand-bound states. The Varkud satellite ribozyme is the most recently solved structure of a known large ribozyme. All puzzles have established biological functions and require structural understanding to appreciate their molecular mechanisms. Through the use of fast-track experimental data, including multidimensional chemical mapping, and accurate prediction of RNA secondary structure, a large portion of the contacts in 3D have been predicted correctly leading to similar topologies for the top ranking predictions. Template-based and homology-derived predictions could predict structures to particularly high accuracies. However, achieving biological insights from de novo prediction of RNA 3D structures still depends on the size and complexity of the RNA. Blind computational predictions of RNA structures already appear to provide useful structural information in many cases. Similar to the previous RNA-Puzzles Round II experiment, the prediction of non-Watson-Crick interactions and the observed high atomic clash scores reveal a notable need for an algorithm of improvement. All prediction models and assessment results are available at http://ahsoka.u-strasbg.fr/rnapuzzles/.

RNA Fibers as Optimized Nanoscaffolds for siRNA Coordination and Reduced Immunological Recognition.

RNA is a versatile biomaterial that can be used to engineer nanoassemblies for personalized treatment of various diseases. Despite promising advancements, the design of RNA nanoassemblies with minimal recognition by the immune system remains a major challenge. Here, an approach is reported to engineer RNA fibrous structures to operate as a customizable platform for efficient coordination of siRNAs and for maintaining low immunostimulation. Functional RNA fibers are studied in silico and their formation is confirmed by various experimental techniques and visualized by atomic force microscopy (AFM). It is demonstrated that the RNA fibers offer multiple advantages among which are: i) programmability and modular design that allow for simultaneous controlled delivery of multiple siRNAs and fluorophores, ii) reduced immunostimulation when compared to other programmable RNA nanoassemblies, and iii) simple production protocol for endotoxin-free fibers with the option of their cotranscriptional assembly. Furthermore, it is shown that functional RNA fibers can be efficiently delivered with various organic and inorganic carriers while retaining their structural integrity in cells. Specific gene silencing triggered by RNA fibers is assessed in human breast cancer and melanoma cell lines, with the confirmed ability of functional fibers to selectively target single nucleotide mutations.

RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures.

This paper is a report of a second round of RNA-Puzzles, a collective and blind experiment in three-dimensional (3D) RNA structure prediction. Three puzzles, Puzzles 5, 6, and 10, represented sequences of three large RNA structures with limited or no homology with previously solved RNA molecules. A lariat-capping ribozyme, as well as riboswitches complexed to adenosylcobalamin and tRNA, were predicted by seven groups using RNAComposer, ModeRNA/SimRNA, Vfold, Rosetta, DMD, MC-Fold, 3dRNA, and AMBER refinement. Some groups derived models using data from state-of-the-art chemical-mapping methods (SHAPE, DMS, CMCT, and mutate-and-map). The comparisons between the predictions and the three subsequently released crystallographic structures, solved at diffraction resolutions of 2.5-3.2 Å, were carried out automatically using various sets of quality indicators. The comparisons clearly demonstrate the state of present-day de novo prediction abilities as well as the limitations of these state-of-the-art methods. All of the best prediction models have similar topologies to the native structures, which suggests that computational methods for RNA structure prediction can already provide useful structural information for biological problems. However, the prediction accuracy for non-Watson-Crick interactions, key to proper folding of RNAs, is low and some predicted models had high Clash Scores. These two difficulties point to some of the continuing bottlenecks in RNA structure prediction. All submitted models are available for download at http://ahsoka.u-strasbg.fr/rnapuzzles/.

iFoldRNA v2: folding RNA with constraints.

UNLABELLED: A key to understanding RNA function is to uncover its complex 3D structure. Experimental methods used for determining RNA 3D structures are technologically challenging and laborious, which makes the development of computational prediction methods of substantial interest. Previously, we developed the iFoldRNA server that allows accurate prediction of short (<50 nt) tertiary RNA structures starting from primary sequences. Here, we present a new version of the iFoldRNA server that permits the prediction of tertiary structure of RNAs as long as a few hundred nucleotides. This substantial increase in the server capacity is achieved by utilization of experimental information such as base-pairing and hydroxyl-radical probing. We demonstrate a significant benefit provided by integration of experimental data and computational methods. AVAILABILITY AND IMPLEMENTATION: http://ifoldrna.dokhlab.org CONTACT: dokh@unc.eu.

Long range correlations and folding angle with applications to α-helical proteins.

The conformational complexity of chain-like macromolecules such as proteins and other linear polymers is much larger than that of point-like atoms and molecules. Unlike particles, chains can bend, twist, and even become knotted. Thus chains might also display a much richer phase structure. Unfortunately, it is not very easy to characterize the phase of a long chain. Essentially, the only known attribute is the radius of gyration. The way how it changes when the degree of polymerization becomes different, and how it evolves when the ambient temperature and solvent properties change, is commonly used to disclose the phase. But in any finite length chain there are corrections to scaling that complicate the detailed analysis of the phase structure. Here we introduce a quantity that we call the folding angle to identify and scrutinize the phase structure, as a complement to the radius of gyration. We argue for a mean-field level relationship between the folding angle and the scaling exponent in the radius of gyration. We then estimate the value of the folding angle in the case of crystallographic α-helical protein structures in the Protein Data Bank. We also show how the experimental value of the folding angle can be obtained computationally, using a semiclassical Born-Oppenheimer description of α-helical chiral chains.

Kinks, loops, and protein folding, with protein A as an example.

The dynamics and energetics of formation of loops in the 46-residue N-terminal fragment of the B-domain of staphylococcal protein A has been studied. Numerical simulations have been performed using coarse-grained molecular dynamics with the united-residue (UNRES) force field. The results have been analyzed in terms of a kink (heteroclinic standing wave solution) of a generalized discrete nonlinear Schrödinger (DNLS) equation. In the case of proteins, the DNLS equation arises from a C(α)-trace-based energy function. Three individual kink profiles were identified in the experimental three-α-helix structure of protein A, in the range of the Glu16-Asn29, Leu20-Asn29, and Gln33-Asn44 residues, respectively; these correspond to two loops in the native structure. UNRES simulations were started from the full right-handed α-helix to obtain a clear picture of kink formation, which would otherwise be blurred by helix formation. All three kinks emerged during coarse-grained simulations. It was found that the formation of each is accompanied by a local free energy increase; this is expressed as the change of UNRES energy which has the physical sense of the potential of mean force of a polypeptide chain. The increase is about 7 kcal/mol. This value can thus be considered as the free energy barrier to kink formation in full α-helical segments of polypeptide chains. During the simulations, the kinks emerge, disappear, propagate, and annihilate each other many times. It was found that the formation of a kink is initiated by an abrupt change in the orientation of a pair of consecutive side chains in the loop region. This resembles the formation of a Bloch wall along a spin chain, where the C(α) backbone corresponds to the chain, and the amino acid side chains are interpreted as the spin variables. This observation suggests that nearest-neighbor side chain-side chain interactions are responsible for initiation of loop formation. It was also found that the individual kinks are reflected as clear peaks in the principal modes of the analyzed trajectory of protein A, the shapes of which resemble the directional derivatives of the kinks along the chain. These observations suggest that the kinks of the DNLS equation determine the functionally important motions of proteins.

On the role of thermal backbone fluctuations in myoglobin ligand gate dynamics.

We construct an energy function that describes the crystallographic structure of sperm whale myoglobin backbone. As a model in our construction, we use the Protein Data Bank entry 1ABS that has been measured at liquid helium temperature. Consequently, the thermal B-factor fluctuations are very small, which is an advantage in our construction. The energy function that we utilize resembles that of the discrete nonlinear Schrödinger equation. Likewise, ours supports topological solitons as local minimum energy configurations. We describe the 1ABS backbone in terms of topological solitons with a precision that deviates from 1ABS by an average root-mean-square distance, which is less than the experimentally observed Debye-Waller B-factor fluctuation distance. We then subject the topological multi-soliton solution to extensive numerical heating and cooling experiments, over a very wide range of temperatures. We concentrate in particular to temperatures above 300 K and below the Θ-point unfolding temperature, which is around 348 K. We confirm that the behavior of the topological multi-soliton is fully consistent with Anfinsen's thermodynamic principle, up to very high temperatures. We observe that the structure responds to an increase of temperature consistently in a very similar manner. This enables us to characterize the onset of thermally induced conformational changes in terms of three distinct backbone ligand gates. One of the gates is made of the helix F and the helix E. The two other gates are chosen similarly, when open they provide a direct access route for a ligand to reach the heme. We find that out of the three gates we investigate, the one which is formed by helices B and G is the most sensitive to thermally induced conformational changes. Our approach provides a novel perspective to the important problem of ligand entry and exit.

Borrowing Transcriptional Kinases to Activate Apoptosis.

Protein kinases are disease drivers whose therapeutic targeting traditionally centers on inhibition of enzymatic activity. Here chemically induced proximity is leveraged to convert kinase inhibitors into context-specific activators of therapeutic genes. Bivalent molecules that link ligands of the transcription factor B-cell lymphoma 6 (BCL6) to ATP-competitive inhibitors of cyclin-dependent kinases (CDKs) were developed to re-localize CDK to BCL6-bound loci on chromatin and direct phosphorylation of RNA Pol II. The resulting BCL6-target proapoptotic gene expression translated into killing of diffuse large B-cell lymphoma (DLBCL) cells at 72 h with EC50s of 0.9 - 10 nM and highly specific ablation of the BCL6-regulated germinal center response in mice. The molecules exhibited 10,000-fold lower cytotoxicity in normal lymphocytes and are well tolerated in mice. Genomic and proteomic evidence corroborated a gain-of-function mechanism where, instead of global enzyme inhibition, a fraction of total kinase activity is borrowed and re-localized to BCL6-bound loci. The strategy demonstrates how kinase inhibitors can be used to context-specifically activate transcription, accessing new therapeutic space.

Coexistence of phases in a protein heterodimer.

A heterodimer consisting of two or more different kinds of proteins can display an enormous number of distinct molecular architectures. The conformational entropy is an essential ingredient in the Helmholtz free energy and, consequently, these heterodimers can have a very complex phase structure. Here, it is proposed that there is a state of proteins, in which the different components of a heterodimer exist in different phases. For this purpose, the structures in the protein data bank (PDB) have been analyzed, with radius of gyration as the order parameter. Two major classes of heterodimers with their protein components coexisting in different phases have been identified. An example is the PDB structure 3DXC. This is a transcriptionally active dimer. One of the components is an isoform of the intra-cellular domain of the Alzheimer-disease related amyloid precursor protein (AICD), and the other is a nuclear multidomain adaptor protein in the Fe65 family. It is concluded from the radius of gyration that neither of the two components in this dimer is in its own collapsed phase, corresponding to a biologically active protein. The UNRES energy function has been utilized to confirm that, if the two components are separated from each other, each of them collapses. The results presented in this work show that heterodimers whose protein components coexist in different phases, can have intriguing physical properties with potentially important biological consequences.