Nuclear receptors and disease: androgen receptor

2004 ◽  
Vol 40 ◽  
pp. 121-136 ◽  
Author(s):  
Bruce Gottlieb ◽  
Lenore K Beitel ◽  
Jianhui Wu ◽  
Youssef A Elhaji ◽  
Mark Trifiro

The androgen receptor (AR) protein regulates transcription of certain genes. Usually this depends upon a central DNA-binding domain that permits the binding of androgen–AR complexes to regulatory DNA sequences near or in a target gene. The AR also has a C-terminal ligand-binding domain and an Nterminal transcription modulatory domain. These N- and C-terminal domains interact directly, and with co-regulatory, non-receptor proteins, to exert precise control over a gene’s transcription rate. The precise roles of these proteins are active research areas. Severe X-linked AR gene (AR) mutations cause complete androgen insensitivity, mild ones impair virilization with or without infertility, and moderate ones yield a wide phenotypic spectrum sometimes among siblings. Different phenotype expressivity may reflect variability of ARinteractive proteins. Mutations occur throughout the AR but are concentrated in specific areas of the gene known as hot spots. A number of these mutations of somatic origin are associated with prostate cancer. N-terminal polyglutamine (polyGln) tract expansion reduces AR transactivation, and when there are more than 38 glutamine residues it causes spinobulbar muscular atrophy, a motor neuron disease, due to a gain of function. Variations in polyGln tract length have been associated as risk factors with prostate, breast, uterine, endometrial and colorectal cancer, as well as male infertility.

PLoS ONE ◽  
2019 ◽  
Vol 14 (6) ◽  
pp. e0218073 ◽  
Author(s):  
Rajiv Movva ◽  
Peyton Greenside ◽  
Georgi K. Marinov ◽  
Surag Nair ◽  
Avanti Shrikumar ◽  
...  

Neuron ◽  
2010 ◽  
Vol 67 (6) ◽  
pp. 936-952 ◽  
Author(s):  
Natalia B. Nedelsky ◽  
Maria Pennuto ◽  
Rebecca B. Smith ◽  
Isabella Palazzolo ◽  
Jennifer Moore ◽  
...  

1997 ◽  
Vol 82 (6) ◽  
pp. 1944-1948
Author(s):  
Michael J. McPhaul ◽  
Hans-Udo Schweikert ◽  
Diane R. Allman

Abstract Mutations of the androgen receptor (AR) cause defects in virilization and can result in a spectrum of phenotypic abnormalities of male sexual development that includes patients with a completely female phenotype (complete testicular feminization) and individuals with less severe defects of virilization, such as Reifenstein syndrome. These phenotypes are not specific for mutations of the AR gene, however, and defects in other genes can also result in similar abnormalities of male development. For this reason, the diagnosis of an AR defect is laborious and requires data from endocrine studies, the family history, and in vitro binding experiments. To assist in the evaluation of patients with possible AR defects, we previously employed the use of a recombinant adenovirus to deliver an androgen-responsive gene into fibroblast cultures to assay AR function in normal subjects and patients with complete forms of androgen resistance. Although these studies demonstrated measurable differences between these two groups of subjects, we did not assay samples from patients with partial defects of androgen action. In the current study, we have modified this method to examine AR function in three groups of patients with known or suspected defects of AR function: patients with Reifenstein syndrome, patients with spinobulbar muscular atrophy, and patients with severe forms of isolated hypospadias. When assayed using this method, the AR function of patients with Reifenstein syndrome was intermediate between that of normal control subjects and that of patients with complete testicular feminization. Using the parameters established by the aforementioned experiments, we found that defective AR function can be detected in fibroblasts established from patients with spinobulbar muscular atrophy and in some patients with severe forms of isolated hypospadias, including two with a normal AR gene sequence. These results suggest that this method may have some utility in screening samples to detect defects of AR function, particularly when viewed in the context of other AR assays results.


2006 ◽  
Vol 34 (6) ◽  
pp. 1098-1102 ◽  
Author(s):  
J. Duff ◽  
P. Davies ◽  
K. Watt ◽  
I.J. McEwan

The AR (androgen receptor) is a ligand-activated transcription factor that mediates the action of the steroids testosterone and dihydrotestosterone. Alterations in the AR gene result in a number of clinical disorders, including: androgen-insensitivity, which leads to disruption of male development; prostate cancer; and a neuromuscular degenerative condition termed spinal bulbar muscular atrophy or Kennedy's disease. The AR gene is X-linked and the protein is coded for by eight exons, giving rise to a C-terminal LBD (ligand-binding domain; exons 4–8), linked by a hinge region (exon 4) to a Zn-finger DBD (DNA-binding domain; exons 2 and 3) and a large structurally distinct NTD (N-terminal domain; exon 1). Identification and characterization of mutations found in prostate cancer and Kennedy's disease patients have revealed the importance of structural dynamics in the mechanisms of action of receptors. Recent results from our laboratory studying genetic changes in the LBD and the structurally flexible NTD will be discussed.


1991 ◽  
Vol 96 (2) ◽  
pp. 162-167 ◽  
Author(s):  
Chuan-Kui Jiang ◽  
Howard S Epstein ◽  
Marjana Tomic ◽  
Irwin M Freedberg ◽  
Miroslav Blumenberg

2018 ◽  
Author(s):  
Rajiv Movva ◽  
Peyton Greenside ◽  
Georgi K. Marinov ◽  
Surag Nair ◽  
Avanti Shrikumar ◽  
...  

AbstractThe relationship between noncoding DNA sequence and gene expression is not well-understood. Massively parallel reporter assays (MPRAs), which quantify the regulatory activity of large libraries of DNA sequences in parallel, are a powerful approach to characterize this relationship. We present MPRA-DragoNN, a convolutional neural network (CNN)-based framework to predict and interpret the regulatory activity of DNA sequences as measured by MPRAs. While our method is generally applicable to a variety of MPRA designs, here we trained our model on the Sharpr-MPRA dataset that measures the activity of ~500,000 constructs tiling 15,720 regulatory regions in human K562 and HepG2 cell lines. MPRA-DragoNN predictions were moderately correlated (Spearman ρ = 0.28) with measured activity and were within range of replicate concordance of the assay. State-of-the-art model interpretation methods revealed high-resolution predictive regulatory sequence features that overlapped transcription factor (TF) binding motifs. We used the model to investigate the cell type and chromatin state preferences of predictive TF motifs. We explored the ability of our model to predict the allelic effects of regulatory variants in an independent MPRA experiment and fine map putative functional SNPs in loci associated with lipid traits. Our results suggest that interpretable deep learning models trained on MPRA data have the potential to reveal meaningful patterns in regulatory DNA sequences and prioritize regulatory genetic variants, especially as larger, higher-quality datasets are produced.


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