Hidden modes of DNA binding by human nuclear receptors

Genomic regions occupied by both RARα and VDR are involved in the convergence and cooperation of retinoid and vitamin D signaling pathways

Retinoic acid receptors (RARs) and the vitamin D receptor (VDR) regulate distinct but overlapping gene sets in multiple cell types. The abundance and characteristics of regulatory regions, occupied by both RARs and VDR are largely unexplored. We used global approaches (ChIP-seq, RNA-seq, and ATAC-seq) and bioinformatics tools to map and characterize common binding regions of RARα and VDR in differentiated human THP-1 cells. We found that the cistromes of ligand-activated RARα and VDR largely overlapped, and their agonists (AM580 and calcitriol) co-regulated several genes, often cooperatively. Common binding regions were frequently (but not exclusively) annotated with co-regulated genes and exhibited increased MED1 occupancy upon ligand stimulation, suggesting their involvement in gene regulation. Chromatin accessibility was typically higher in the common regions than in regions occupied exclusively by RARα or VDR. DNA response elements for RARα (DR1/2/5) and VDR (DR3) were enriched in the common regions, albeit the co-occurrence of the two types of canonical motifs was low (8.4%), suggesting that "degenerate" DR1/2/5 and DR3 motifs or other sequences could mediate the binding. In summary, common binding regions of RARα and VDR are at the crossroads of the retinoid and vitamin D pathways, playing important roles in their convergence and cooperation.

Targeting Androgen, Thyroid Hormone, and Vitamin A and D Receptors to Treat Prostate Cancer

The nuclear hormone family of receptors regulates gene expression. The androgen receptor (AR), upon ligand binding and homodimerization, shuttles from the cytosol into the nucleus to activate gene expression. Thyroid hormone receptors (TRs), retinoic acid receptors (RARs), and the vitamin D receptor (VDR) are present in the nucleus bound to chromatin as a heterodimer with the retinoid X receptors (RXRs) and repress gene expression. Ligand binding leads to transcription activation. The hormonal ligands for these receptors play crucial roles to ensure the proper conduct of very many tissues and exert effects on prostate cancer (PCa) cells. Androgens support PCa proliferation and androgen deprivation alone or with chemotherapy is the standard therapy for PCa. RARγ activation and 3,5,3'-triiodo-L-thyronine (T3) stimulation of TRβ support the growth of PCa cells. Ligand stimulation of VDR drives growth arrest, differentiation, and apoptosis of PCa cells. Often these receptors are explored as separate avenues to find treatments for PCa and other cancers. However, there is accumulating evidence to support receptor interactions and crosstalk of regulatory events whereby a better understanding might lead to new combinatorial treatments.

Nuclear Receptors and the Hidden Language of the Metabolome

Nuclear hormone receptors (NHRs) are a family of ligand-regulated transcription factors that control key aspects of development and physiology. The regulation of NHRs by ligands derived from metabolism or diet makes them excellent pharmacological targets, and the mechanistic understanding of how NHRs interact with their ligands to regulate downstream gene networks, along with the identification of ligands for orphan NHRs, could enable innovative approaches for cellular engineering, disease modeling and regenerative medicine. We review recent discoveries in the identification of physiologic ligands for NHRs. We propose new models of ligand-receptor co-evolution, the emergence of hormonal function and models of regulation of NHR specificity and activity via one-ligand and two-ligand models as well as feedback loops. Lastly, we discuss limitations on the processes for the identification of physiologic NHR ligands and emerging new methodologies that could be used to identify the natural ligands for the remaining 17 orphan NHRs in the human genome.

Structural mechanism underlying variations in DNA binding by the androgen receptor

The androgen receptor (AR) is a steroid activated transcription factor which recognizes DNA motifs resembling inverted repeats of a conserved 5'-AGAACA-3'-like hexanucleotides separated by a three-nucleotide spacer from a similar, but less conserved hexanucleotide. Here, we report the structures of the human AR DNA binding domain (DBD) bound to two natural AREs (C3 and MTV) in head-to-head dimer conformations, diffracting at 2.05 Å and 2.25 Å, respectively. These structures help to explain the impact of androgen insensitivity mutations on the structure integrity, DNA binding and DBD dimerization. The binding affinity of the AR DBD to different DNA motifs were measured by the BioLayer Interferometry (BLI) and further validated by Molecular Dynamics (MD) simulations. This shows that the high binding affinity of the first DBD to the upstream 5'-AGAACA-3' motif induces the cooperative binding of the second DBD to the second hexanucleotide. Our data indicate identical interaction of the DBDs to the upstream hexanucleotides, while forming an induced closer contact of the second DBD on the non-canonical hexanucleotides. The variation in binding between the DBD monomers are the result of differences in DNA occupancy, protein-protein interactions, DNA binding affinity, and DNA binding energy profiles. We propose this has functional consequences.

Using synthetic genome readers/regulators to interrogate chromatin processes: A brief review

Aberrant gene expression underlies numerous human ailments. Hence, developing small molecules to target and remedy dysfunctional gene regulation has been a long-standing goal at the interface of chemistry and medicine. A major challenge for designing small molecule therapeutics aimed at targeting desired genomic loci is the minimization of widescale disruption of genomic functions. To address this challenge, we rationally design polyamide-based multi-functional molecules, i.e., Synthetic Genome Readers/Regulators (SynGRs), which, by design, target distinct sequences in the genome. Herein, we briefly review how SynGRs access chromatin-bound and chromatin-free genomic sites, then highlight the methods for the study of chromatin processes using SynGRs on positioned nucleosomes in vitro or disease-causing repressive genomic loci in vivo.

Structural characterization of the DNA binding mechanism of retinoic acid-related orphan receptor gamma

Retinoic acid-related orphan receptor gamma (RORγ) plays critical roles in regulating various biological processes and has been linked to immunodeficiency disorders and cancers. DNA recognition is essential for RORγ to exert its functions. However, the underlying mechanism of the DNA binding by RORγ remains unclear. In this study, we present the crystal structure of the complex of RORγ1 DNA-binding domain (RORγ1-DBD)/direct repeat DNA element DR2 at 2.3 Å resolution. We demonstrate that RORγ1-DBD binds the DR2 motif as a homodimer, with the C-terminal extension (CTE) region of RORγ1-DBD contributing to the DNA recognition and the formation of dimeric interface. Further studies reveal that REV-ERB-DBD and RXR-DBD, also bind the DR2 site as a homodimer, while NR4A2-DBD binds DR2 as a monomer. Our research uncovers a binding mechanism of RORγ1 to the DR2 site and provides insights into the biological functions of RORγ1 and the broader RORs subfamily.

Predicting DNA structure using a deep learning method

Understanding the mechanisms of protein-DNA binding is critical in comprehending gene regulation. Three-dimensional DNA structure, also described as DNA shape, plays a key role in these mechanisms. In this study, we present a deep learning-based method, Deep DNAshape, that fundamentally changes the current k-mer based high-throughput prediction of DNA shape features by accurately accounting for the influence of extended flanking regions, without the need for extensive molecular simulations or structural biology experiments. By using the Deep DNAshape method, DNA structural features can be predicted for any length and number of DNA sequences in a high-throughput manner, providing an understanding of the effects of flanking regions on DNA structure in a target region of a sequence. The Deep DNAshape method provides access to the influence of distant flanking regions on a region of interest. Our findings reveal that DNA shape readout mechanisms of a core target are quantitatively affected by flanking regions, including extended flanking regions, providing valuable insights into the detailed structural readout mechanisms of protein-DNA binding. Furthermore, when incorporated in machine learning models, the features generated by Deep DNAshape improve the model prediction accuracy. Collectively, Deep DNAshape can serve as versatile and powerful tool for diverse DNA structure-related studies.

Deep DNAshape: Predicting DNA shape considering extended flanking regions using a deep learning method

Understanding the mechanisms of protein-DNA binding is critical in comprehending gene regulation. Three-dimensional DNA shape plays a key role in these mechanisms. In this study, we present a deep learning-based method, Deep DNAshape, that fundamentally changes the current k -mer based high-throughput prediction of DNA shape features by accurately accounting for the influence of extended flanking regions, without the need for extensive molecular simulations or structural biology experiments. By using the Deep DNAshape method, refined DNA shape features can be predicted for any length and number of DNA sequences in a high-throughput manner, providing a deeper understanding of the effects of flanking regions on DNA shape in a target region of a sequence. Deep DNAshape method provides access to the influence of distant flanking regions on a region of interest. Our findings reveal that DNA shape readout mechanisms of a core target are quantitatively affected by flanking regions, including extended flanking regions, providing valuable insights into the detailed structural readout mechanisms of protein-DNA binding. Furthermore, when incorporated in machine learning models, the features generated by Deep DNAshape improve the model prediction accuracy. Collectively, Deep DNAshape can serve as a versatile and powerful tool for diverse DNA structure-related studies.