scholarly journals Modulation of chromatin position and gene expression by HDAC4 interaction with nucleoporins

2011 ◽  
Vol 193 (1) ◽  
pp. 21-29 ◽  
Author(s):  
Izhak Kehat ◽  
Federica Accornero ◽  
Bruce J. Aronow ◽  
Jeffery D. Molkentin
Keyword(s):  
Gene Expression ◽  
Histone Deacetylases ◽  
Target Genes ◽  
Expression Patterns ◽  
Cardiomyocyte Hypertrophy ◽  
Specific Gene ◽  
Interacting Protein ◽  
Genome Wide ◽  
Promoter Occupancy ◽  
Class Iia Hdacs

Class IIa histone deacetylases (HDACs) can modulate chromatin architecture and transcriptional activity, thereby participating in the regulation of cellular responses such as cardiomyocyte hypertrophy. However, the target genes of class IIa HDACs that control inducible cardiac growth and the broader mechanisms whereby these deacetylases modulate locus-specific gene expression within chromatin remain a mystery. Here, we used genome-wide promoter occupancy analysis, expression profiling, and primary cell validation to identify direct class IIa HDAC4 targets in cardiomyocytes. Simultaneously, we identified nucleoporin155 (Nup155) as an HDAC4-interacting protein. Mechanistically, we show that HDAC4 modulated the association of identified target genes with nucleoporins through interaction with Nup155. Moreover, a truncated mutant of Nup155 that cannot bind HDAC4 suppressed HDAC4-induced gene expression patterns and chromatin–nucleoporin association, suggesting that Nup155-mediated localization was required for HDAC4’s effect on gene expression. We thus propose a novel mechanism of action for HDAC4, suggesting it can function to dynamically regulate gene expression through changes in chromatin–nucleoporin association.

scholarly journals Beyond the ENCODE project: using genomics and epigenomics strategies to study enhancer evolution

2013 ◽  
Vol 368 (1632) ◽  
pp. 20130022 ◽  
Author(s):  
Noboru Jo Sakabe ◽  
Marcelo A. Nobrega
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Expression Patterns ◽  
Dna Interaction ◽  
Regulatory Elements ◽  
Specific Gene ◽  
Genome Wide ◽  
Identify Transcription Factor ◽  
Specific Proteins ◽  
Species Specific

The complex expression patterns observed for many genes are often regulated by distal transcription enhancers. Changes in the nucleotide sequences of enhancers may therefore lead to changes in gene expression, representing a central mechanism by which organisms evolve. With the development of the experimental technique of chromatin immunoprecipitation (ChIP), in which discrete regions of the genome bound by specific proteins can be identified, it is now possible to identify transcription factor binding events (putative cis -regulatory elements) in entire genomes. Comparing protein–DNA binding maps allows us, for the first time, to attempt to identify regulatory differences and infer global patterns of change in gene expression across species. Here, we review studies that used genome-wide ChIP to study the evolution of enhancers. The trend is one of high divergence of cis -regulatory elements between species, possibly compensated by extensive creation and loss of regulatory elements and rewiring of their target genes. We speculate on the meaning of the differences observed and discuss that although ChIP experiments identify the biochemical event of protein–DNA interaction, it cannot determine whether the event results in a biological function, and therefore more studies are required to establish the effect of divergence of binding events on species-specific gene expression.

scholarly journals Identification of key tissue-specific, biological processes by integrating enhancer information in maize gene regulatory networks

2020 ◽  
Author(s):  
Maud Fagny ◽  
Marieke Lydia Kuijjer ◽  
Maike Stam ◽  
Johann Joets ◽  
Olivier Turc ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Binding Sites ◽  
Regulatory Network ◽  
Tissue Specificity ◽  
Target Genes ◽  
Specific Gene ◽  
Tissue Specific ◽  
Genome Wide ◽  
Gene Regulatory

AbstractEnhancers are important regulators of gene expression during numerous crucial processes including tissue differentiation across development. In plants, their recent molecular characterization revealed their capacity to activate the expression of several target genes through the binding of transcription factors. Nevertheless, identifying these target genes at a genome-wide level remains a challenge, in particular in species with large genomes, where enhancers and target genes can be hundreds of kilobases away. Therefore, the contribution of enhancers to regulatory network is still poorly understood in plants. In this study, we investigate the enhancer-driven regulatory network of two maize tissues at different stages: leaves at seedling stage and husks (bracts) at flowering. Using a systems biology approach, we integrate genomic, epigenomic and transcriptomic data to model the regulatory relationship between transcription factors and their potential target genes. We identify regulatory modules specific to husk and V2-IST, and show that they are involved in distinct functions related to the biology of each tissue. We evidence enhancers exhibiting binding sites for two distinct transcription factor families (DOF and AP2/ERF) that drive the tissue-specificity of gene expression in seedling immature leaf and husk. Analysis of the corresponding enhancer sequences reveals that two different transposable element families (TIR transposon Mutator and MITE Pif/Harbinger) have shaped the regulatory network in each tissue, and that MITEs have provided new transcription factor binding sites that are involved in husk tissue-specificity.SignificanceEnhancers play a major role in regulating tissue-specific gene expression in higher eukaryotes, including angiosperms. While molecular characterization of enhancers has improved over the past years, identifying their target genes at the genome-wide scale remains challenging. Here, we integrate genomic, epigenomic and transcriptomic data to decipher the tissue-specific gene regulatory network controlled by enhancers at two different stages of maize leaf development. Using a systems biology approach, we identify transcription factor families regulating gene tissue-specific expression in husk and seedling leaves, and characterize the enhancers likely to be involved. We show that a large part of maize enhancers is derived from transposable elements, which can provide novel transcription factor binding sites crucial to the regulation of tissue-specific biological functions.

scholarly journals CT-FOCS: a novel method for inferring cell type-specific enhancer-promoter maps

2019 ◽  
Author(s):  
Tom Aharon Hait ◽  
Ran Elkon ◽  
Ron Shamir
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Expression Patterns ◽  
Cell Types ◽  
Specific Gene ◽  
Cell Type ◽  
Chromatin Interactions ◽  
Cell Type Specific ◽  
Novel Method ◽  
Validation Scheme

AbstractSpatiotemporal gene expression patterns are governed to a large extent by enhancer elements, typically located distally from their target genes. Identification of enhancer-promoter (EP) links that are specific and functional in individual cell types is a key challenge in understanding gene regulation. We introduce CT-FOCS, a new statistical inference method that utilizes multiple replicates per cell type to infer cell type-specific EP links. Computationally predicted EP links are usually benchmarked against experimentally determined chromatin interactions measured by ChIA-PET and promoter-capture HiC techniques. We expand this validation scheme by using also loops that overlap in their anchor sites. In analyzing 1,366 samples from ENCODE, Roadmap epigenomics and FANTOM5, CT-FOCS inferred highly cell type-specific EP links more accurately than state-of-the-art methods. We illustrate how our inferred EP links drive cell type-specific gene expression and regulation.

scholarly journals Genome-Wide Phylogenetic Analysis, Expression Pattern, and Transcriptional Regulatory Network of the Pig C/EBP Gene Family

2021 ◽  
Vol 17 ◽  
pp. 117693432110413
Author(s):  
Chaoxin Zhang ◽  
Tao Wang ◽  
Tongyan Cui ◽  
Shengwei Liu ◽  
Bing Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Phylogenetic Analysis ◽  
Regulatory Network ◽  
Target Genes ◽  
Transcriptional Regulatory Network ◽  
Expression Patterns ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Genome Wide ◽  
A Genome

The CCAAT/enhancer binding protein (C/EBP) transcription factors (TFs) regulate many important biological processes, such as energy metabolism, inflammation, cell proliferation etc. A genome-wide gene identification revealed the presence of a total of 99 C/EBP genes in pig and 19 eukaryote genomes. Phylogenetic analysis showed that all C/EBP TFs were classified into 6 subgroups named C/EBPα, C/EBPβ, C/EBPδ, C/EBPε, C/EBPγ, and C/EBPζ. Gene expression analysis showed that the C/EBPα, C/EBPβ, C/EBPδ, C/EBPγ, and C/EBPζ genes were expressed ubiquitously with inconsistent expression patterns in various pig tissues. Moreover, a pig C/EBP regulatory network was constructed, including C/EBP genes, TFs and miRNAs. A total of 27 feed-forward loop (FFL) motifs were detected in the pig C/EBP regulatory network. Based on the RNA-seq data, gene expression patterns related to FFL sub-network were analyzed in 27 adult pig tissues. Certain FFL motifs may be tissue specific. Functional enrichment analysis indicated that C/EBP and its target genes are involved in many important biological pathways. These results provide valuable information that clarifies the evolutionary relationships of the C/EBP family and contributes to the understanding of the biological function of C/EBP genes.

scholarly journals A genome-wide transcriptional analysis of morphology determination inCandida albicans

2013 ◽  
Vol 24 (3) ◽  
pp. 246-260 ◽  
Author(s):  
Patricia L. Carlisle ◽  
David Kadosh
Keyword(s):  
Gene Expression ◽  
Fungal Infections ◽  
Transcriptional Profiling ◽  
Expression Patterns ◽  
Global Gene Expression ◽  
Transcriptional Analysis ◽  
Specific Gene ◽  
Genome Wide ◽  
A Genome ◽  
Genetically Manipulated

Candida albicans, the most common cause of human fungal infections, undergoes a reversible morphological transition from yeast to pseudohyphal and hyphal filaments, which is required for virulence. For many years, the relationship among global gene expression patterns associated with determination of specific C. albicans morphologies has remained obscure. Using a strain that can be genetically manipulated to sequentially transition from yeast to pseudohyphae to hyphae in the absence of complex environmental cues and upstream signaling pathways, we demonstrate by whole-genome transcriptional profiling that genes associated with pseudohyphae represent a subset of those associated with hyphae and are generally expressed at lower levels. Our results also strongly suggest that in addition to dosage, extended duration of filament-specific gene expression is sufficient to drive the C. albicans yeast-pseudohyphal-hyphal transition. Finally, we describe the first transcriptional profile of the C. albicans reverse hyphal-pseudohyphal-yeast transition and demonstrate that this transition involves not only down-regulation of known hyphal-specific, genes but also differential expression of additional genes that have not previously been associated with the forward transition, including many involved in protein synthesis. These findings provide new insight into genome-wide expression patterns important for determining fungal morphology and suggest that in addition to similarities, there are also fundamental differences in global gene expression as pathogenic filamentous fungi undergo forward and reverse morphological transitions.

scholarly journals Changes in DNA Methylation in Response to 6-Benzylaminopurine Affect Allele-Specific Gene Expression in Populus tomentosa

2020 ◽  
Author(s):  
Anran Xuan ◽  
Yuepeng Song ◽  
Chenhao Bu ◽  
Panfei Chen ◽  
Yousry A. El-Kassaby ◽  
...  
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Expression Patterns ◽  
Populus Tomentosa ◽  
Differentially Expressed ◽  
Specific Gene ◽  
Snp Analysis ◽  
Specific Gene Expression ◽  
Genome Wide ◽  
Allele Specific

The cytokinins play important roles in plant growth and development by regulating gene expression at genome wide level. DNA methylation is responsive to the external environment, but whether DNA methylation changes in response to cytokinin treatment to regulate gene expression is still unclear. Here, we used bisulfite sequencing and RNA sequencing to examine genome-wide DNA methylation and gene expression patterns in poplar (Populus tomentosa) after treatment with the synthetic cytokinin 6-benzylaminopurine (6-BA). We identified 566 significantly differentially methylated regions (DMRs) in response to 6-BA treatment. Transcriptome analysis showed that 501 protein-coding genes, 262 long non-coding RNAs, and 15,793 24-nt small interfering RNAs were differentially expressed under 6-BA treatment. Among these, 79% were differentially expressed between alleles in P. tomentosa. Combined DNA methylation and gene expression analysis demonstrated that DNA methylation plays an important role in regulating allele-specific gene expression. To further investigate the relationship between these 6-BA-responsive genes and phenotypic variation, we performed SNP analysis of 507 6-BA-responsive DMRs via re-sequencing using a natural population of P. tomentosa and identified 206 SNPs that were significantly associated with growth and wood properties. Association analysis indicated that 53% of loci with allele-specific expression had primarily dominant effects on poplar traits. Our comprehensive analyses of P. tomentosa DNA methylation and the regulation of allele-specific gene expression suggest that DNA methylation is an important regulator of imbalanced expression between allelic loci.

Genome-wide transcriptome mapping analysis identifies organ-specific gene expression patterns along human chromosomes

Genomics ◽  
2004 ◽  
Vol 84 (5) ◽  
pp. 867-875 ◽  
Author(s):  
Taro Yamashita ◽  
Masao Honda ◽  
Hajime Takatori ◽  
Ryuhei Nishino ◽  
Nobuaki Hoshino ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Specific Gene ◽  
Human Chromosomes ◽  
Specific Gene Expression ◽  
Genome Wide ◽  
Mapping Analysis ◽  
Organ Specific

scholarly journals Dynamic Interactions of Transcription Factors and Enhancer Reprogramming in Cancer Progression

2021 ◽  
Vol 11 ◽  
Author(s):  
Emily Zboril ◽  
Hannah Yoo ◽  
Lizhen Chen ◽  
Zhijie Liu
Keyword(s):  
Gene Expression ◽  
Cancer Progression ◽  
Expression Patterns ◽  
Therapy Resistance ◽  
Regulatory Elements ◽  
Specific Gene ◽  
Dynamic Interactions ◽  
Genome Wide ◽  
Dna Regulatory Elements

While improved tumor treatment has significantly reduced the overall mortality rates, invasive progression including recurrence, therapy resistance and metastasis contributes to the majority of deaths caused by cancer. Enhancers are essential distal DNA regulatory elements that control temporal- or spatial-specific gene expression patterns during development and other biological processes. Genome-wide sequencing has revealed frequent alterations of enhancers in cancers and reprogramming of distal enhancers has emerged as one of the important features for tumors. In this review, we will discuss tumor progression-associated enhancer dynamics, its transcription factor (TF) drivers and how enhancer reprogramming modulates gene expression during cancer invasive progression. Additionally, we will explore recent advancements in contemporary technology including single-cell sequencing, spatial transcriptomics and CUT&RUN, which have permitted integrated studies of enhancer reprogramming in vivo. Given the essential roles of enhancer dynamics and its drivers in controlling cancer progression and treatment outcome, understanding these changes will be paramount in mitigating invasive events and discovering novel therapeutic targets.

scholarly journals Identifying Transcriptomic Correlates of Histology using Deep Learning

2020 ◽  
Author(s):  
Liviu Badea ◽  
Emil Stănescu
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Genome Wide ◽  
Gene Perturbations ◽  
Whole Slide Images

AbstractLinking phenotypes to specific gene expression profiles is an extremely important problem in biology, which has been approached mainly by correlation methods or, more fundamentally, by studying the effects of gene perturbations. However, genome-wide perturbations involve extensive experimental efforts, which may be prohibitive for certain organisms. On the other hand, the characterization of the various phenotypes frequently requires an expert’s subjective interpretation, such as a histopathologist’s description of tissue slide images in terms of complex visual features (e.g. ‘acinar structures’). In this paper, we use Deep Learning to eliminate the inherent subjective nature of these visual histological features and link them to genomic data, thus establishing a more precisely quantifiable correlation between transcriptomes and phenotypes. Using a dataset of whole slide images with matching gene expression data from 39 normal tissue types, we first developed a Deep Learning tissue classifier with an accuracy of 94%. Then we searched for genes whose expression correlates with features inferred by the classifier and demonstrate that Deep Learning can automatically derive visual (phenotypical) features that are well correlated with the transcriptome and therefore biologically interpretable. As we are particularly concerned with interpretability and explainability of the inferred histological models, we also develop visualizations of the inferred features and compare them with gene expression patterns determined by immunohistochemistry. This can be viewed as a first step toward bridging the gap between the level of genes and the cellular organization of tissues.

scholarly journals Identifying transcriptomic correlates of histology using deep learning

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242858
Author(s):  
Liviu Badea ◽  
Emil Stănescu
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Gene Expression Profiles ◽  
Specific Gene ◽  
Genome Wide ◽  
Gene Perturbations ◽  
Whole Slide Images

Linking phenotypes to specific gene expression profiles is an extremely important problem in biology, which has been approached mainly by correlation methods or, more fundamentally, by studying the effects of gene perturbations. However, genome-wide perturbations involve extensive experimental efforts, which may be prohibitive for certain organisms. On the other hand, the characterization of the various phenotypes frequently requires an expert’s subjective interpretation, such as a histopathologist’s description of tissue slide images in terms of complex visual features (e.g. ‘acinar structures’). In this paper, we use Deep Learning to eliminate the inherent subjective nature of these visual histological features and link them to genomic data, thus establishing a more precisely quantifiable correlation between transcriptomes and phenotypes. Using a dataset of whole slide images with matching gene expression data from 39 normal tissue types, we first developed a Deep Learning tissue classifier with an accuracy of 94%. Then we searched for genes whose expression correlates with features inferred by the classifier and demonstrate that Deep Learning can automatically derive visual (phenotypical) features that are well correlated with the transcriptome and therefore biologically interpretable. As we are particularly concerned with interpretability and explainability of the inferred histological models, we also develop visualizations of the inferred features and compare them with gene expression patterns determined by immunohistochemistry. This can be viewed as a first step toward bridging the gap between the level of genes and the cellular organization of tissues.

Sign in / Sign up

Export Citation Format

Share Document