scholarly journals Discovering human transcription factor interactions with genetic variants, novel DNA motifs, and repetitive elements using enhanced yeast one-hybrid assays

2018 ◽  
Author(s):  
Shaleen Shrestha ◽  
Jared Allan Sewell ◽  
Clarissa Stephanie Santoso ◽  
Elena Forchielli ◽  
Sebastian Carrasco Pro ◽  
...  

ABSTRACTIdentifying transcription factor (TF) binding to noncoding variants, uncharacterized DNA motifs, and repetitive genomic elements has been technically and computationally challenging. Current experimental methods, such as chromatin immunoprecipitation, generally test one TF at a time, and computational motif algorithms often lead to false positive and negative predictions. To address these limitations, we developed two approaches based on enhanced yeast one-hybrid assays. The first approach interrogates the binding of >1,000 human TFs to repetitive DNA elements, while the second evaluates TF binding to single nucleotide variants, short insertions and deletions (indels), and novel DNA motifs. Using the first approach, we detected the binding of 75 TFs, including several nuclear hormone receptors and ETS factors, to the highly repetitive Alu elements. Using the second approach, we identified cancer-associated changes in TF binding, including gain of interactions involving ETS TFs and loss of interactions involving KLF TFs to different mutations in the TERT promoter, and gain of a MYB interaction with an 18 bp indel in the TAL1 super-enhancer. Additionally, we identified the TFs that bind to three uncharacterized DNA motifs identified in DNase footprinting assays. We anticipate that these approaches will expand our capabilities to study genetic variation and under-characterized genomic regions.

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Hong Wang ◽  
Aiping Duan ◽  
Jing Zhang ◽  
Qi Wang ◽  
Yuexian Xing ◽  
...  

AbstractElucidating transcription mediated by the glucocorticoid receptor (GR) is crucial for understanding the role of glucocorticoids (GCs) in the treatment of diseases. Podocyte is a useful model for studying GR regulation because GCs are the primary medication for podocytopathy. In this study, we integrated data from transcriptome, transcription factor binding, histone modification, and genome topology. Our data reveals that the GR binds and activates selective regulatory elements in podocyte. The 3D interactome captured by HiChIP facilitates the identification of remote targets of GR. We found that GR in podocyte is enriched at transcriptional interaction hubs and super-enhancers. We further demonstrate that the target gene of the top GR-associated super-enhancer is indispensable to the effective functioning of GC in podocyte. Our findings provided insights into the mechanisms underlying the protective effect of GCs on podocyte, and demonstrate the importance of considering transcriptional interactions in order to fine-map regulatory networks of GR.


Biochemistry ◽  
2009 ◽  
Vol 48 (9) ◽  
pp. 1975-1983 ◽  
Author(s):  
Kenneth L. Seldeen ◽  
Caleb B. McDonald ◽  
Brian J. Deegan ◽  
Amjad Farooq

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Yavor K. Bozhilov ◽  
Damien J. Downes ◽  
Jelena Telenius ◽  
A. Marieke Oudelaar ◽  
Emmanuel N. Olivier ◽  
...  

AbstractMany single nucleotide variants (SNVs) associated with human traits and genetic diseases are thought to alter the activity of existing regulatory elements. Some SNVs may also create entirely new regulatory elements which change gene expression, but the mechanism by which they do so is largely unknown. Here we show that a single base change in an otherwise unremarkable region of the human α-globin cluster creates an entirely new promoter and an associated unidirectional transcript. This SNV downregulates α-globin expression causing α-thalassaemia. Of note, the new promoter lying between the α-globin genes and their associated super-enhancer disrupts their interaction in an orientation-dependent manner. Together these observations show how both the order and orientation of the fundamental elements of the genome determine patterns of gene expression and support the concept that active genes may act to disrupt enhancer-promoter interactions in mammals as in Drosophila. Finally, these findings should prompt others to fully evaluate SNVs lying outside of known regulatory elements as causing changes in gene expression by creating new regulatory elements.


2002 ◽  
Vol 6 (4) ◽  
pp. 491-495 ◽  
Author(s):  
Gerhard Behre ◽  
Venkateshwar A Reddy ◽  
Daniel G Tenen ◽  
Wolfgang Hiddemann ◽  
Abdul A Peer Zada ◽  
...  

2018 ◽  
Author(s):  
Mehran Karimzadeh ◽  
Michael M. Hoffman

AbstractMotivationIdentifying transcription factor binding sites is the first step in pinpointing non-coding mutations that disrupt the regulatory function of transcription factors and promote disease. ChIP-seq is the most common method for identifying binding sites, but performing it on patient samples is hampered by the amount of available biological material and the cost of the experiment. Existing methods for computational prediction of regulatory elements primarily predict binding in genomic regions with sequence similarity to known transcription factor sequence preferences. This has limited efficacy since most binding sites do not resemble known transcription factor sequence motifs, and many transcription factors are not even sequence-specific.ResultsWe developed Virtual ChIP-seq, which predicts binding of individual transcription factors in new cell types using an artificial neural network that integrates ChIP-seq results from other cell types and chromatin accessibility data in the new cell type. Virtual ChIP-seq also uses learned associations between gene expression and transcription factor binding at specific genomic regions. This approach outperforms methods that predict TF binding solely based on sequence preference, pre-dicting binding for 36 transcription factors (Matthews correlation coefficient > 0.3).AvailabilityThe datasets we used for training and validation are available at https://virchip.hoffmanlab.org. We have deposited in Zenodo the current version of our software (http://doi.org/10.5281/zenodo.1066928), datasets (http://doi.org/10.5281/zenodo.823297), predictions for 36 transcription factors on Roadmap Epigenomics cell types (http://doi.org/10.5281/zenodo.1455759), and predictions in Cistrome as well as ENCODE-DREAM in vivo TF Binding Site Prediction Challenge (http://doi.org/10.5281/zenodo.1209308).


1992 ◽  
Vol 103 (1) ◽  
pp. 9-14 ◽  
Author(s):  
K.A. Lee

Dimeric transcription factors that bind to DNA are often grouped into families on the basis of dimerization and DNA-binding specificities. cDNA cloning studies have established that members of the same family have structurally related dimerisation and DNA-binding domains but diverge in other regions that are important for transcriptional activation. These features lead to the straightforward suggestion that although all members of a family bind to similar DNA elements, individual members exhibit distinct transcriptional effector functions. This simple view is now supported by experimental evidence from those systems that have proved amenable to study. There are however some largely unaddressed questions that concern the mechanisms that allow family members to go about their business without interference from their highly related siblings. Here I will discuss some insights from studies of the bZIP class of transcription factors.


ESC CardioMed ◽  
2018 ◽  
pp. 669-671
Author(s):  
Eric Schulze-Bahr

The human genome consists of approximately 3 billion (3 × 109) base pairs of DNA (around 20,000 genes), organized as 23 chromosomes (diploid parental set), and a small mitochondrial genome (37 genes, including 13 proteins; 16,589 base pairs) of maternal origin. Most human genetic variation is natural, that is, common or rare (minor allele frequency >0.1%) and does not cause disease—apart from every true disease-causing (bona fide) mutation each individual genome harbours more than 3.5 million single nucleotide variants (including >10,000 non-synonymous changes causing amino acid substitutions) and 200–300 large structural or copy number variants (insertions/deletions, up to several thousands of base-pairs) that are non-disease-causing variations and scattered throughout coding and non-coding genomic regions.


Sign in / Sign up

Export Citation Format

Share Document