scholarly journals Functional effects of variation in transcription factor binding highlight long-range gene regulation by epromoters

2020 ◽  
Vol 48 (6) ◽  
pp. 2866-2879 ◽  
Author(s):  
Joanna Mitchelmore ◽  
Nastasiya F Grinberg ◽  
Chris Wallace ◽  
Mikhail Spivakov
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Statistical Power ◽  
Target Genes ◽  
Allelic Variation ◽  
Regulatory Function ◽  
Regulatory Sequences ◽  
Distal Enhancer ◽  
Association Testing ◽  
Gene Regulatory

Abstract Identifying DNA cis-regulatory modules (CRMs) that control the expression of specific genes is crucial for deciphering the logic of transcriptional control. Natural genetic variation can point to the possible gene regulatory function of specific sequences through their allelic associations with gene expression. However, comprehensive identification of causal regulatory sequences in brute-force association testing without incorporating prior knowledge is challenging due to limited statistical power and effects of linkage disequilibrium. Sequence variants affecting transcription factor (TF) binding at CRMs have a strong potential to influence gene regulatory function, which provides a motivation for prioritizing such variants in association testing. Here, we generate an atlas of CRMs showing predicted allelic variation in TF binding affinity in human lymphoblastoid cell lines and test their association with the expression of their putative target genes inferred from Promoter Capture Hi-C and immediate linear proximity. We reveal >1300 CRM TF-binding variants associated with target gene expression, the majority of them undetected with standard association testing. A large proportion of CRMs showing associations with the expression of genes they contact in 3D localize to the promoter regions of other genes, supporting the notion of ‘epromoters’: dual-action CRMs with promoter and distal enhancer activity.

scholarly journals Functional effects of variation in transcription factor binding highlight long-range gene regulation by epromoters

2019 ◽  
Author(s):  
Joanna Mitchelmore ◽  
Nastasiya Grinberg ◽  
Chris Wallace ◽  
Mikhail Spivakov
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Statistical Power ◽  
Target Genes ◽  
Allelic Variation ◽  
Regulatory Function ◽  
Regulatory Sequences ◽  
Distal Enhancer ◽  
Association Testing ◽  
Gene Regulatory

AbstractIdentifying DNA cis-regulatory modules (CRMs) that control the expression of specific genes is crucial for deciphering the logic of transcriptional control. Natural genetic variation can point to the possible gene regulatory function of specific sequences through their allelic associations with gene expression. However, comprehensive identification of causal regulatory sequences in brute-force association testing without incorporating prior knowledge is challenging due to limited statistical power and effects of linkage disequilibrium. Sequence variants affecting transcription factor (TF) binding at CRMs have a strong potential to influence gene regulatory function, which provides a motivation for prioritising such variants in association testing. Here, we generate an atlas of CRMs showing predicted allelic variation in TF binding affinity in human lymphoblastoid cell lines (LCLs) and test their association with the expression of their putative target genes inferred from Promoter Capture Hi-C and immediate linear proximity. We reveal over 1300 CRM TF-binding variants associated with target gene expression, the majority of them undetected with standard association testing. A large proportion of CRMs showing associations with the expression of genes they contact in 3D localise to the promoter regions of other genes, supporting the notion of ‘epromoters’: dual-action CRMs with promoter and distal enhancer activity.

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 Modeling transcriptional regulation using gene regulatory networks based on multi-omics data sources

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Neel Patel ◽  
William S. Bush
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Transcriptional Regulation ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Prediction Models ◽  
Long Distance ◽  
Chromatin Looping ◽  
Gene Regulatory ◽  
The Impact

Abstract Background Transcriptional regulation is complex, requiring multiple cis (local) and trans acting mechanisms working in concert to drive gene expression, with disruption of these processes linked to multiple diseases. Previous computational attempts to understand the influence of regulatory mechanisms on gene expression have used prediction models containing input features derived from cis regulatory factors. However, local chromatin looping and trans-acting mechanisms are known to also influence transcriptional regulation, and their inclusion may improve model accuracy and interpretation. In this study, we create a general model of transcription factor influence on gene expression by incorporating both cis and trans gene regulatory features. Results We describe a computational framework to model gene expression for GM12878 and K562 cell lines. This framework weights the impact of transcription factor-based regulatory data using multi-omics gene regulatory networks to account for both cis and trans acting mechanisms, and measures of the local chromatin context. These prediction models perform significantly better compared to models containing cis-regulatory features alone. Models that additionally integrate long distance chromatin interactions (or chromatin looping) between distal transcription factor binding regions and gene promoters also show improved accuracy. As a demonstration of their utility, effect estimates from these models were used to weight cis-regulatory rare variants for sequence kernel association test analyses of gene expression. Conclusions Our models generate refined effect estimates for the influence of individual transcription factors on gene expression, allowing characterization of their roles across the genome. This work also provides a framework for integrating multiple data types into a single model of transcriptional regulation.

scholarly journals Role of NFAT in the Progression of Diabetic Atherosclerosis

2021 ◽  
Vol 8 ◽  
Author(s):  
Yaoyao Cai ◽  
Haipeng Yao ◽  
Zhen Sun ◽  
Ying Wang ◽  
Yunyun Zhao ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Cardiovascular System ◽  
Signaling Pathways ◽  
Immune Cells ◽  
Target Genes ◽  
Vascular System ◽  
Foam Cell ◽  
Vascular Diseases ◽  
Regulatory Function ◽  
Foam Cell Formation

Nuclear factor of activated T cells (NFAT) is a transcription factor with a multidirectional regulatory function, that is widely expressed in immune cells, including cells in the cardiovascular system, and non-immune cells. A large number of studies have confirmed that calcineurin/NFAT signal transduction is very important in the development of vascular system and cardiovascular system during embryonic development, and plays some role in the occurrence of vascular diseases such as atherosclerosis, vascular calcification, and hypertension. Recent in vitro and in vivo studies have shown that NFAT proteins and their activation in the nucleus and binding to DNA-related sites can easily ɨnduce the expression of downstream target genes that participate in the proliferation, migration, angiogenesis, and vascular inflammation of vascular wall related cells in various pathophysiological states. NFAT expression is regulated by various signaling pathways, including CD137-CD137L, and OX40-OX40L pathways. As a functionally diverse transcription factor, NFAT interacts with a large number of signaling molecules to modulate intracellular and extracellular signaling pathways. These NFAT-centered signaling pathways play important regulatory roles in the progression of atherosclerosis, such as in vascular smooth muscle cell phenotypic transition and migration, endothelial cell injury, macrophage-derived foam cell formation, and plaque calcification. NFAT and related signaling pathways provide new therapeutic targets for vascular diseases such as atherosclerosis. Hence, further studies of the mechanism of NFAT in the occurrence and evolution of atherosclerosis remain crucial.

scholarly journals Strigolactone GR24 upregulates Nrf2 target genes and may protect against oxidative stress in skeletal muscle

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 1459
Author(s):  
Shalem Raju Modi ◽  
Tarja Kokkola
Keyword(s):  
Oxidative Stress ◽  
Gene Expression ◽  
Skeletal Muscle ◽  
Transcription Factor ◽  
Stress Responses ◽  
Mycorrhizal Fungi ◽  
Target Genes ◽  
Redox Homeostasis ◽  
Antioxidant Defences ◽  
Microarray Gene Expression

GR24 is a synthetic strigolactone analog, demonstrated to regulate the development of plants and arbuscular mycorrhizal fungi. GR24 possesses anti-cancer and anti-apoptotic properties, enhances insulin sensitivity and mitochondrial biogenesis in skeletal myotubes, inhibits adipogenesis, decreases inflammation in adipocytes and macrophages and downregulates the expression of hepatic gluconeogenic enzymes. Transcription factor Nrf2 (Nuclear factor (erythroid-derived 2)-like 2) is a master regulator of antioxidant response, regulating a multitude of genes involved in cellular stress responses and anti-inflammatory pathways, thus maintaining cellular redox homeostasis. Nrf2 activation reduces the deleterious effects of mitochondrial toxins and has multiple roles in promoting mitochondrial function and dynamics. We studied the role of GR24 on gene expression in rat L6 skeletal muscle cells which were differentiated into myotubes. The myotubes were treated with GR24 and analyzed by microarray gene expression profiling. GR24 upregulated the cytoprotective transcription factor Nrf2 and its target genes, activating antioxidant defences, suggesting that GR24 may protect skeletal muscle from the toxic effects of oxidative stress.

scholarly journals Identifying genetic modulators of the connectivity between transcription factors and their transcriptional targets

2016 ◽  
Vol 113 (13) ◽  
pp. E1835-E1843 ◽  
Author(s):  
Mina Fazlollahi ◽  
Ivor Muroff ◽  
Eunjee Lee ◽  
Helen C. Causton ◽  
Harmen J. Bussemaker
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Target Genes ◽  
Regulation Of Gene Expression ◽  
Nonsynonymous Mutation ◽  
Gene Regulatory ◽  
Genetic Modulators

Regulation of gene expression by transcription factors (TFs) is highly dependent on genetic background and interactions with cofactors. Identifying specific context factors is a major challenge that requires new approaches. Here we show that exploiting natural variation is a potent strategy for probing functional interactions within gene regulatory networks. We developed an algorithm to identify genetic polymorphisms that modulate the regulatory connectivity between specific transcription factors and their target genes in vivo. As a proof of principle, we mapped connectivity quantitative trait loci (cQTLs) using parallel genotype and gene expression data for segregants from a cross between two strains of the yeast Saccharomyces cerevisiae. We identified a nonsynonymous mutation in the DIG2 gene as a cQTL for the transcription factor Ste12p and confirmed this prediction empirically. We also identified three polymorphisms in TAF13 as putative modulators of regulation by Gcn4p. Our method has potential for revealing how genetic differences among individuals influence gene regulatory networks in any organism for which gene expression and genotype data are available along with information on binding preferences for transcription factors.

scholarly journals The TEA Transcription Factor Tec1 Confers Promoter-Specific Gene Regulation by Ste12-Dependent and -Independent Mechanisms

2010 ◽  
Vol 9 (4) ◽  
pp. 514-531 ◽  
Author(s):  
Barbara Heise ◽  
Julia van der Felden ◽  
Sandra Kern ◽  
Mario Malcher ◽  
Stefan Brückner ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Transcriptional Activation ◽  
Binding Sites ◽  
Target Genes ◽  
Specific Gene ◽  
Dependent Manner ◽  
A Genome ◽  
Regulated Gene Expression

ABSTRACT In Saccharomyces cerevisiae, the TEA transcription factor Tec1 is known to regulate target genes together with a second transcription factor, Ste12. Tec1-Ste12 complexes can activate transcription through Tec1 binding sites (TCSs), which can be further combined with Ste12 binding sites (PREs) for cooperative DNA binding. However, previous studies have hinted that Tec1 might regulate transcription also without Ste12. Here, we show that in vivo, physiological amounts of Tec1 are sufficient to stimulate TCS-mediated gene expression and transcription of the FLO11 gene in the absence of Ste12. In vitro, Tec1 is able to bind TCS elements with high affinity and specificity without Ste12. Furthermore, Tec1 contains a C-terminal transcriptional activation domain that confers Ste12-independent activation of TCS-regulated gene expression. On a genome-wide scale, we identified 302 Tec1 target genes that constitute two distinct classes. A first class of 254 genes is regulated by Tec1 in a Ste12-dependent manner and is enriched for genes that are bound by Tec1 and Ste12 in vivo. In contrast, a second class of 48 genes can be regulated by Tec1 independently of Ste12 and is enriched for genes that are bound by the stress transcription factors Yap6, Nrg1, Cin5, Skn7, Hsf1, and Msn4. Finally, we find that combinatorial control by Tec1-Ste12 complexes stabilizes Tec1 against degradation. Our study suggests that Tec1 is able to regulate TCS-mediated gene expression by Ste12-dependent and Ste12-independent mechanisms that enable promoter-specific transcriptional control.

scholarly journals Expresso: A database and web server for exploring the interaction of transcription factors and their target genes in Arabidopsis thaliana using ChIP-Seq peak data

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 372 ◽  
Author(s):  
Delasa Aghamirzaie ◽  
Karthik Raja Velmurugan ◽  
Shuchi Wu ◽  
Doaa Altarawy ◽  
Lenwood S. Heath ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Transcription Factors ◽  
Chromatin Immunoprecipitation ◽  
Target Genes ◽  
Regulation Of Gene Expression ◽  
Data Sets ◽  
Motif Analysis ◽  
Chromatin Immunoprecipitation Sequencing

Motivation: The increasing availability of chromatin immunoprecipitation sequencing (ChIP-Seq) data enables us to learn more about the action of transcription factors in the regulation of gene expression. Even though in vivo transcriptional regulation often involves the concerted action of more than one transcription factor, the format of each individual ChIP-Seq dataset usually represents the action of a single transcription factor. Therefore, a relational database in which available ChIP-Seq datasets are curated is essential. Results: We present Expresso (database and webserver) as a tool for the collection and integration of available Arabidopsis ChIP-Seq peak data, which in turn can be linked to a user’s gene expression data. Known target genes of transcription factors were identified by motif analysis of publicly available GEO ChIP-Seq data sets. Expresso currently provides three services: 1) Identification of target genes of a given transcription factor; 2) Identification of transcription factors that regulate a gene of interest; 3) Computation of correlation between the gene expression of transcription factors and their target genes. Availability: Expresso is freely available at http://bioinformatics.cs.vt.edu/expresso/

scholarly journals Co-expression analysis reveals interpretable gene modules controlled by trans-acting genetic variants

2020 ◽  
Author(s):  
Liis Kolberg ◽  
Nurlan Kerimov ◽  
Hedi Peterson ◽  
Kaur Alasoo
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Expression Analysis ◽  
Complex Disease ◽  
Target Genes ◽  
Disease Onset ◽  
Functional Enrichment ◽  
Eqtl Analysis ◽  
Cell Type ◽  
Motif Analysis

AbstractBackgroundDeveloping novel therapies for complex disease requires better understanding of the causal processes that contribute to disease onset and progression. Although trans-acting gene expression quantitative trait loci (trans-eQTLs) can be a powerful approach to directly reveal cellular processes modulated by disease variants, detecting trans-eQTLs remains challenging due to their small effect sizes and large number of genes tested. However, if a single trans-eQTL controls a group of co-regulated genes, then multiple testing burden can be greatly reduced by summarising gene expression at the level of co-expression modules prior to trans-eQTL analysis.ResultsWe analysed gene expression and genotype data from six blood cell types from 226 to 710 individuals. We inferred gene co-expression modules with five methods on the full dataset, as well as in each cell type separately. We detected a number of established co-expression module trans-eQTLs, such as the monocyte-specific associations at the IFNB1 and LYZ loci, as well as a platelet-specific ARHGEF3 locus associated with mean platelet volume. We also discovered a novel trans association near the SLC39A8 gene in LPS-stimulated monocytes. Here, we linked an early-response cis-eQTL of the SLC39A8 gene to a module of co-expressed metallothionein genes upregulated more than 20 hours later and used motif analysis to identify zinc-induced activation of the MTF1 transcription factor as a likely mediator of this effect.ConclusionsOur analysis provides a rare detailed characterisation of a trans-eQTL effect cascade from a proximal cis effect to the affected signalling pathway, transcription factor, and target genes. This highlights how co-expression analysis combined with functional enrichment analysis can greatly improve the identification and prioritisation of trans-eQTLs when applied to emerging cell-type specific datasets.

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