scholarly journals A combination of transcription factors mediates inducible interchromosomal pairing

2018 ◽  
Author(s):  
Seungsoo Kim ◽  
Maitreya J Dunham ◽  
Jay Shendure
Keyword(s):  
Transcription Factors ◽  
Binding Site ◽  
Molecular Mechanism ◽  
Dna Sequences ◽  
Three Dimensional ◽  
Chromosome Conformation ◽  
Homolog Pairing ◽  
A Genome ◽  
Repressor Complex ◽  
Necessary And Sufficient

SummaryRemodeling of the three-dimensional organization of a genome has been previously described (e.g. condition-specific pairing or looping), but it remains unknown which factors specify and mediate such shifts in chromosome conformation. Here we describe an assay, MAP-C (Mutation Analysis in Pools by Chromosome conformation capture), that enables the simultaneous characterization of hundreds of cis or trans-acting mutations for their effects on a chromosomal contact or loop. As a proof of concept, we applied MAP-C to systematically dissect the molecular mechanism of inducible interchromosomal pairing between HAS1pr-TDA1pr alleles in Saccharomyces yeast. We identified three transcription factors, Leu3, Sdd4 (Ypr022c), and Rgt1, whose collective binding to nearby DNA sequences is necessary and sufficient for inducible pairing between binding site clusters. Rgt1 contributes to the regulation of pairing, both through changes in expression level and through its interactions with the Tup1/Ssn6 repressor complex. HAS1pr-TDA1pr is the only locus with a cluster of binding site motifs for all three factors in both S. cerevisiae and S. uvarum genomes, but the promoter for HXT3, which contains Leu3 and Rgt1 motifs, also exhibits inducible homolog pairing. Altogether, our results demonstrate that specific combinations of transcription factors can mediate condition-specific interchromosomal contacts, and reveal a molecular mechanism for interchromosomal contacts and mitotic homolog pairing.

A 3-Bp Deletion Between Transcription Factor Binding Motifs In the HBS1L-MYB Intergenic Region on Chromosome 6q23 Is Associated with HbF Expression

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 1013-1013
Author(s):  
John J. Farrell ◽  
Richard M. Sherva ◽  
Zhi-yi Chen ◽  
Luo Hong-yuan ◽  
Banjamin F. Chu ◽  
...  
Keyword(s):  
Hong Kong ◽  
Transcription Factors ◽  
Dna Sequences ◽  
Globin Gene ◽  
Gene Promoter ◽  
Proximal Promoter ◽  
Luciferase Reporter ◽  
Deletion Polymorphism ◽  
Functional Motif ◽  
A Genome

Abstract Abstract 1013 More than 3% of Chinese in Hong Kong are heterozygous carriers of β-thalassemia. Homozygotes or compound heterozygotes for β-thalassemia are usually severely ill and require monthly transfusions. Increased production of fetal hemoglobin (HbF) can modulate the disease severity by compensating for the shortfall of HbA caused by the β-thalassemia mutations. HbF level in adults varies and is regulated as a multigenic trait. Three major HbF quantitative trait loci (QTL) have been identified: the C/T SNP also known as the Xmn I site at the Gγ-globin gene promoter, the BCL11A polymorphism on chromosome 2p16, and the HBS1L-MYB intergenic polymorphism (HMIP) on chromosome 6q23. The functional motif for each of these 3 QTLs responsible for their effects upon HbF is not known. We undertook a genome-wide association study (GWAS), using Illumina Human 610-Quad BeadChip array, on 619 Chinese β-thalassemia heterozygotes from Hong Kong. In this population, the variance in HbF due to HMIP is 13.5%, significantly higher than that due to BCL11A polymorphism (6.4%). We used 1,000 Genomes Project data, SNP imputation, comparisons of association results across populations, predicted binding of transcription factors, and phylogenetic conservation to identify the functional variant in HMIP. Based on these lines of evidence, a hitherto unreported association between HbF expression and a 3-bp deletion on chromosome 6q23 was found. In 335 Chinese β-thalassemia heterozygotes, the 3-bp deletion polymorphism is in complete linkage disequilibrium with rs9399137, the SNP found in multiple GWAS to be most significantly associated with HbF (P=1.4E-24 in the Chinese cohort GWAS). Flanking this deletion are conserved binding sites for TAL1/SCL1, E47, GATA, and RUNX1/AML1, which are essential erythropoiesis-related transcription factors. The 3-bp deletion changes the normal DNA binding configuration of these transcription factors and spatial configuration for DNA-protein binding and/or protein-protein interactions. Furthermore, this 3-bp deletion polymorphism resides within a likely erythroid distal regulatory region manifested by DNase I hypersensitivity and GATA-1 binding (Wahlberg et al, Blood 114:1254, 2009). We hypothesized that a 61-bp fragment of DNA that encompasses the site of the 3-bp deletion polymorphism might have enhancer-like activity. When ligated to the Gγ-globin gene 1.4 kb proximal promoter linked to a luciferase reporter gene, the 61-bp fragment of DNA enhances the Gγ-globin gene promoter activity by more than 3-fold after transient transfection into K562 cells. A 58-bp fragment of DNA that includes the 3-bp deletion has 60% more enhancer-like activity than the 61-bp fragment without the deletion. These findings suggest that this 3-bp deletion polymorphism is most likely the functional motif accounting for HMIP modulation of HbF. Further studies are needed to identify target genes for this enhancer-like activity mediated by the DNA sequences encompassing the 3-bp deletion polymorphism in HMIP. These studies also suggest that this experimental approach could be used to identify functional motifs in other genotype-phenotype association studies. Disclosures: No relevant conflicts of interest to declare.

scholarly journals The dynamic three-dimensional organization of the diploid yeast genome

2016 ◽  
Author(s):  
Seungsoo Kim ◽  
Ivan Liachko ◽  
Donna G Brickner ◽  
Kate Cook ◽  
William S Noble ◽  
...  
Keyword(s):  
Nuclear Periphery ◽  
Three Dimensional ◽  
Culture Conditions ◽  
Yeast Genome ◽  
Nuclear Pore ◽  
Nuclear Pore Complexes ◽  
Chromosome Conformation ◽  
Homolog Pairing ◽  
Pore Complexes ◽  
Eukaryotic Genomes

AbstractThe budding yeast Saccharomyces cerevisiae is a long-standing model for the three-dimensional organization of eukaryotic genomes. Even in this well-studied model, it is unclear how homolog pairing in diploids and environment-induced gene relocalization influence overall genome organization. Here, we performed high-throughput chromosome conformation capture on diverged Saccharomyces hybrid diploids to obtain the first global view of chromosome conformation in diploid yeasts. After controlling for the Rabl-like orientation, we observe significant homolog proximity that increased in saturated culture conditions. Surprisingly, we observe a localized increase in homologous interactions between the HAS1 alleles specifically under galactose induction and saturated growth, mediated by association with nuclear pore complexes at the nuclear periphery. Together, these results reveal that the diploid yeast genome has a dynamic and complex 3D organization.

scholarly journals Structural features of the fly chromatin colors revealed by automatic three-dimensional modeling.

2016 ◽  
Author(s):  
François Serra ◽  
Davide Baù ◽  
Guillaume Filion ◽  
Marc A. Marti-Renom
Keyword(s):  
Spatial Organization ◽  
Three Dimensional ◽  
Structural Features ◽  
Three Dimensions ◽  
Chromosome Conformation ◽  
Three Dimensional Modeling ◽  
Genomic Technologies ◽  
A Genome ◽  
Contact Data ◽  
Three Dimensional Models

The sequence of a genome is insufficient to understand all genomic processes carried out in the cell nucleus. To achieve this, the knowledge of its three- dimensional architecture is necessary. Advances in genomic technologies and the development of new analytical methods, such as Chromosome Conformation Capture (3C) and its derivatives, now permit to investigate the spatial organization of genomes. However, inferring structures from raw contact data is a tedious process for shortage of available tools. Here we present TADbit, a computational framework to analyze and model the chromatin fiber in three dimensions. To illustrate the use of TADbit, we automatically modeled 50 genomic domains from the fly genome revealing differential structural features of the previously defined chromatin colors, establishing a link between the conformation of the genome and the local chromatin composition. More generally, TADbit allows to obtain three-dimensional models ready for visualization from 3C-based experiments and to characterize their relation to gene expression and epigenetic states. TADbit is open-source and available for download from http://www.3DGenomes.org.

scholarly journals Structure of the RZZ complex and molecular basis of Spindly-driven corona assembly at human kinetochores

2021 ◽  
Author(s):  
Tobias Raisch ◽  
Giuseppe Ciossani ◽  
Ennio d’Amico ◽  
Verena Cmentowski ◽  
Sara Carmignani ◽  
...  
Keyword(s):  
High Resolution ◽  
Binding Site ◽  
Molecular Mechanism ◽  
Building Block ◽  
Molecular Basis ◽  
Spindle Assembly Checkpoint ◽  
Spindle Assembly ◽  
Structural Requirements ◽  
Necessary And Sufficient

In metazoans, a ≍1 megadalton (MDa) super-complex comprising the Dynein-Dynactin adaptor Spindly and the ROD-Zwilch-ZW10 (RZZ) complex is the building block of a fibrous biopolymer, the kinetochore fibrous corona. The corona assembles on mitotic kinetochores to promote microtubule capture and spindle assembly checkpoint (SAC) signaling. We report here a high-resolution cryo-EM structure that captures the essential features of the RZZ complex, including a farnesyl binding site required for Spindly binding. Using a highly predictive in vitro assay, we demonstrate that the SAC kinase MPS1 is necessary and sufficient for corona assembly at supercritical concentrations of the RZZ-Spindly (RZZS) complex, and describe the molecular mechanism of phosphorylation-dependent filament nucleation. We identify several structural requirements for RZZS polymerization in rings and sheets. Finally, we identify determinants of kinetochore localization and corona assembly of Spindly. Our results describe a framework for the long-sought-for molecular basis of corona assembly on metazoan kinetochores.

scholarly journals DeepGRN: Prediction of transcription factor binding site across cell-types using attention-based deep neural networks 

2021 ◽  
Author(s):  
Chen Chen ◽  
Jie Hou ◽  
Xiaowen Shi ◽  
Hua Yang ◽  
James A. Birchler ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Binding Site ◽  
Transcription Factor Binding Site ◽  
Dna Sequences ◽  
Binding Sites ◽  
Transcription Factor Binding ◽  
Parallel Sequencing ◽  
Factor Binding Site ◽  
Factor Binding

Abstract BackgroundDue to the complexity of the biological systems, the prediction of the potential DNA binding sites for transcription factors remains a difficult problem in computational biology. Genomic DNA sequences and experimental results from parallel sequencing provide available information about the affinity and accessibility of genome and are commonly used features in binding sites prediction. The attention mechanism in deep learning has shown its capability to learn long-range dependencies from sequential data, such as sentences and voices. Until now, no study has applied this approach in binding site inference from massively parallel sequencing data. The successful applications of attention mechanism in similar input contexts motivate us to build and test new methods that can accurately determine the binding sites of transcription factors.ResultsIn this study, we propose a novel tool (named DeepGRN) for transcription factors binding site prediction based on the combination of two components: single attention module and pairwise attention module. The performance of our methods is evaluated on the ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge datasets. The results show that DeepGRN achieves higher unified scores in 6 of 13 targets than any of the top four methods in the DREAM challenge. We also demonstrate that the attention weights learned by the model are correlated with potential informative inputs, such as DNase-Seq coverage and motifs, which provide possible explanations for the predictive improvements in DeepGRN.ConclusionsDeepGRN can automatically and effectively predict transcription factor binding sites from DNA sequences and DNase-Seq coverage. Furthermore, the visualization techniques we developed for the attention modules help to interpret how critical patterns from different types of input features are recognized by our model.

Cement gland-specific activation of the Xag1 promoter is regulated by co-operation of putative Ets and ATF/CREB transcription factors

Development ◽  
2002 ◽  
Vol 129 (19) ◽  
pp. 4387-4397
Author(s):  
Fiona C. Wardle ◽  
Daniel H. Wainstock ◽  
Hazel L. Sive
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Transcription Factors ◽  
Binding Site ◽  
Reporter Gene ◽  
Binding Sites ◽  
Cement Gland ◽  
Specific Expression ◽  
Necessary And Sufficient ◽  
Do So

The cement gland marks the extreme anterior ectoderm of the Xenopus embryo, and is determined through the overlap of several positional domains. In order to understand how these positional cues activate cement gland differentiation, the promoter of Xag1, a marker of cement gland differentiation, was analyzed. Previous studies have shown that Xag1 expression can be activated by the anterior-specific transcription factor Otx2, but that this activation is indirect. 102 bp of upstream genomic Xag1 sequence restricts reporter gene expression specifically to the cement gland. Within this region, putative binding sites for Ets and ATF/CREB transcription factors are both necessary and sufficient to drive cement gland-specific expression, and cooperate to do so. Furthermore, while the putative ATF/CREB factor is activated by Otx2, a factor acting through the putative Ets-binding site is not. These results suggest that Ets-like and ATF/CREB-like family members play a role in regulating Xag1 expression in the cement gland, through integration of Otx2 dependent and independent pathways.

scholarly journals DeepGRN: prediction of transcription factor binding site across cell-types using attention-based deep neural networks

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Chen Chen ◽  
Jie Hou ◽  
Xiaowen Shi ◽  
Hua Yang ◽  
James A. Birchler ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Binding Site ◽  
Transcription Factor Binding Site ◽  
Dna Sequences ◽  
Binding Sites ◽  
Transcription Factor Binding ◽  
Parallel Sequencing ◽  
Factor Binding Site ◽  
Factor Binding

Abstract Background Due to the complexity of the biological systems, the prediction of the potential DNA binding sites for transcription factors remains a difficult problem in computational biology. Genomic DNA sequences and experimental results from parallel sequencing provide available information about the affinity and accessibility of genome and are commonly used features in binding sites prediction. The attention mechanism in deep learning has shown its capability to learn long-range dependencies from sequential data, such as sentences and voices. Until now, no study has applied this approach in binding site inference from massively parallel sequencing data. The successful applications of attention mechanism in similar input contexts motivate us to build and test new methods that can accurately determine the binding sites of transcription factors. Results In this study, we propose a novel tool (named DeepGRN) for transcription factors binding site prediction based on the combination of two components: single attention module and pairwise attention module. The performance of our methods is evaluated on the ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge datasets. The results show that DeepGRN achieves higher unified scores in 6 of 13 targets than any of the top four methods in the DREAM challenge. We also demonstrate that the attention weights learned by the model are correlated with potential informative inputs, such as DNase-Seq coverage and motifs, which provide possible explanations for the predictive improvements in DeepGRN. Conclusions DeepGRN can automatically and effectively predict transcription factor binding sites from DNA sequences and DNase-Seq coverage. Furthermore, the visualization techniques we developed for the attention modules help to interpret how critical patterns from different types of input features are recognized by our model.

scholarly journals Prediction of transcription factor binding site across cell-types using attention-based deep neural networks

2020 ◽  
Author(s):  
Chen Chen ◽  
Jie Hou ◽  
Xiaowen Shi ◽  
Hua Yang ◽  
James A. Birchler ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Binding Site ◽  
Transcription Factor Binding Site ◽  
Dna Sequences ◽  
Binding Sites ◽  
Transcription Factor Binding ◽  
Parallel Sequencing ◽  
Factor Binding Site ◽  
Factor Binding

Abstract Background Due to the complexity of the biological systems, the prediction of the potential DNA binding sites for transcription factors remains a difficult problem in computational biology. Genomic DNA sequences and experimental results from parallel sequencing provide available information about the affinity and accessibility of genome and are commonly used features in binding sites prediction. The attention mechanism in deep learning has shown its capability to learn long-range dependencies from sequential data, such as sentences and voices. Until now, no study has applied this approach in binding site inference from massively parallel sequencing data. The successful applications of attention mechanism in similar input contexts motivate us to build and test new methods that can accurately determine the binding sites of transcription factors. Results In this study, we propose a novel tool (named DeepGRN) for transcription factors binding site prediction based on the combination of two components: single attention module and pairwise attention module. The performance of our methods is evaluated on the ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge datasets. The results show that DeepGRN achieves higher unified scores in 6 of 13 targets than any of the top four methods in the DREAM challenge. We also demonstrate that the attention weights learned by the model are correlated with potential informative inputs, such as DNase-Seq coverage and motifs, which provide possible explanations for the predictive improvements in DeepGRN. Conclusions DeepGRN can automatically and effectively predict transcription factor binding sites from DNA sequences and DNase-Seq coverage. Furthermore, the visualization techniques we developed for the attention modules help to interpret how critical patterns from different types of input features are recognized by our model.

scholarly journals GSDB: a database of 3D chromosome and genome structures reconstructed from Hi-C data

2019 ◽  
Author(s):  
Oluwatosin Oluwadare ◽  
Max Highsmith ◽  
Jianlin Cheng
Keyword(s):  
Structure Prediction ◽  
Biological Activities ◽  
Genome Structure ◽  
3D Structure ◽  
Three Dimensional ◽  
Chromosome Conformation ◽  
3D Genome ◽  
Reconstruction Methods ◽  
A Genome ◽  
Structure Reconstruction

ABSTRACTAdvances in the study of chromosome conformation capture (3C) technologies, such as Hi-C technique - capable of capturing chromosomal interactions in a genome-wide scale - have led to the development of three-dimensional (3D) chromosome and genome structure reconstruction methods from Hi-C data. The 3D genome structure is important because it plays a role in a variety of important biological activities such as DNA replication, gene regulation, genome interaction, and gene expression. In recent years, numerous Hi-C datasets have been generated, and likewise, a number of genome structure construction algorithms have been developed. However, until now, there has been no freely available repository for 3D chromosome structures. In this work, we outline the construction of a novel Genome Structure Database (GSDB) to create a comprehensive repository that contains 3D structures for Hi-C datasets constructed by a variety of 3D structure reconstruction tools. GSDB contains over 50,000 structures constructed by 12 state-of-the-art chromosome and genome structure prediction methods for publicly used Hi-C datasets with varying resolution. The database is useful for the community to study the function of genome from a 3D perspective. GSDB is accessible at http://sysbio.rnet.missouri.edu/3dgenome/GSDB

scholarly journals Insights into Gene Regulatory Networks in Chondrocytes

2019 ◽  
Vol 20 (24) ◽  
pp. 6324 ◽  
Author(s):  
Hironori Hojo ◽  
Shinsuke Ohba
Keyword(s):  
Transcription Factors ◽  
Dna Sequences ◽  
Regulatory Networks ◽  
Developmental Process ◽  
Regulatory Elements ◽  
Regulatory Mechanisms ◽  
Gene Expressions ◽  
A Genome ◽  
Gene Regulatory Elements ◽  
Gene Regulatory

Chondrogenesis is a key developmental process that molds the framework of our body and generates the skeletal tissues by coupling with osteogenesis. The developmental processes are well-coordinated by spatiotemporal gene expressions, which are hardwired with gene regulatory elements. Those elements exist as thousands of modules of DNA sequences on the genome. Transcription factors function as key regulatory proteins by binding to regulatory elements and recruiting cofactors. Over the past 30 years, extensive attempts have been made to identify gene regulatory mechanisms in chondrogenesis, mainly through biochemical approaches and genetics. More recently, newly developed next-generation sequencers (NGS) have identified thousands of gene regulatory elements on a genome scale, and provided novel insights into the multiple layers of gene regulatory mechanisms, including the modes of actions of transcription factors, post-translational histone modifications, chromatin accessibility, the concept of pioneer factors, and three-dimensional chromatin architecture. In this review, we summarize the studies that have improved our understanding of the gene regulatory mechanisms in chondrogenesis, from the historical studies to the more recent works using NGS. Finally, we consider the future perspectives, including efforts to improve our understanding of the gene regulatory landscape in chondrogenesis and potential applications to the treatment of chondrocyte-related diseases.

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