scholarly journals Constructing Gene Regulatory Networks using Epigenetic Data

2020 ◽  
Author(s):  
Abhijeet Rajendra Sonawane ◽  
Dawn L. DeMeo ◽  
John Quackenbush ◽  
Kimberly Glass
Keyword(s):  
Transcription Factor ◽  
Gene Regulatory Networks ◽  
Message Passing ◽  
Regulatory Networks ◽  
Transcription Factor Binding ◽  
Chromatin Accessibility ◽  
Sequence Motif ◽  
Epigenetic Information ◽  
Factor Binding ◽  
Gene Regulatory

AbstractThe biological processes that drive cellular function can be represented by a complex network of interactions between regulators (transcription factors) and their targets (genes). A cell’s epigenetic state plays an important role in mediating these interactions, primarily by influencing chromatin accessibility. However, effectively leveraging epigenetic information when constructing regulatory networks remains a challenge. We developed SPIDER, which incorporates epigenetic information (DNase-Seq) into a message passing framework in order to estimate gene regulatory networks. We validated SPIDER’s predictions using ChlP-Seq data from ENCODE and found that SPIDER networks were more accurate than other publicly available, epigenetically informed regulatory networks as well as networks based on methods that leverage epigenetic data to predict transcription factor binding sites. SPIDER was also able to improve the detection of cell line specific regulatory interactions. Notably, SPIDER can recover ChlP-seq verified transcription factor binding events in the regulatory regions of genes that do not have a corresponding sequence motif. Constructing biologically interpretable, epigenetically informed networks using SPIDER will allow us to better understand gene regulation as well as aid in the identification of cell-specific drivers and biomarkers of cellular phenotypes.

scholarly journals Constructing gene regulatory networks using epigenetic data

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Abhijeet Rajendra Sonawane ◽  
Dawn L. DeMeo ◽  
John Quackenbush ◽  
Kimberly Glass
Keyword(s):  
Transcription Factor ◽  
Gene Regulatory Networks ◽  
Regulatory Network ◽  
Message Passing ◽  
Regulatory Networks ◽  
Transcription Factor Binding ◽  
Network Reconstruction ◽  
Sequence Motif ◽  
Factor Binding ◽  
Gene Regulatory

AbstractThe biological processes that drive cellular function can be represented by a complex network of interactions between regulators (transcription factors) and their targets (genes). A cell’s epigenetic state plays an important role in mediating these interactions, primarily by influencing chromatin accessibility. However, how to effectively use epigenetic data when constructing a gene regulatory network remains an open question. Almost all existing network reconstruction approaches focus on estimating transcription factor to gene connections using transcriptomic data. In contrast, computational approaches for analyzing epigenetic data generally focus on improving transcription factor binding site predictions rather than deducing regulatory network relationships. We bridged this gap by developing SPIDER, a network reconstruction approach that incorporates epigenetic data into a message-passing framework to estimate gene regulatory networks. We validated SPIDER’s predictions using ChIP-seq data from ENCODE and found that SPIDER networks are both highly accurate and include cell-line-specific regulatory interactions. Notably, SPIDER can recover ChIP-seq verified transcription factor binding events in the regulatory regions of genes that do not have a corresponding sequence motif. The networks estimated by SPIDER have the potential to identify novel hypotheses that will allow us to better characterize cell-type and phenotype specific regulatory mechanisms.

scholarly journals Cross-species analysis of melanoma enhancer logic using deep learning

2019 ◽  
Author(s):  
Liesbeth Minnoye ◽  
Ibrahim Ihsan Taskiran ◽  
David Mauduit ◽  
Maurizio Fazio ◽  
Linde Van Aerschot ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Melanoma Cell ◽  
Gene Regulatory Networks ◽  
Binding Sites ◽  
Regulatory Networks ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Factor Binding ◽  
Depth Analysis ◽  
Gene Regulatory

AbstractGenomic enhancers form the central nodes of gene regulatory networks by harbouring combinations of transcription factor binding sites. Deciphering the combinatorial code by which these binding sites are assembled within enhancers is indispensable to understand their regulatory involvement in establishing a cell’s phenotype, especially within biological systems with dysregulated gene regulatory networks, such as melanoma. In order to unravel the enhancer logic of the two most common melanoma cell states, namely the melanocytic and mesenchymal-like state, we combined comparative epigenomics with machine learning. By profiling chromatin accessibility using ATAC-seq on a cohort of 27 melanoma cell lines across six different species, we demonstrate the conservation of the two main melanoma states and their underlying master regulators. To perform an in-depth analysis of the enhancer architecture, we trained a deep neural network, called DeepMEL, to classify melanoma enhancers not only in the human genome, but also in other species. DeepMEL revealed the presence, organisation and positional specificity of important transcription factor binding sites. Together, this extensive analysis of the melanoma enhancer code allowed us to propose the concept of a core regulatory complex binding to melanocytic enhancers, consisting of SOX10, TFAP2A, MITF and RUNX, and to disentangle their individual roles in regulating enhancer accessibility and activity.

scholarly journals TRACE: transcription factor footprinting using chromatin accessibility data and DNA sequence

2019 ◽  
Author(s):  
Ningxin Ouyang ◽  
Alan P. Boyle
Keyword(s):  
Transcription Factor ◽  
Binding Sites ◽  
Regulatory Networks ◽  
Transcription Factor Binding Sites ◽  
Transcription Factor Binding ◽  
Chromatin Accessibility ◽  
Training Data ◽  
Distinct Advantage ◽  
Factor Binding ◽  
Dna Elements

AbstractTranscription is tightly regulated by cis-regulatory DNA elements where transcription factors can bind. Thus, identification of transcription factor binding sites is key to understanding gene expression and whole regulatory networks within a cell. The standard approaches for transcription factor binding sites (TFBSs) prediction such as position weight matrices (PWMs) and chromatin immunoprecipitation followed by sequencing (ChIP-seq) are widely used but have their drawbacks such as high false positive rates and limited antibody availability, respectively. Several computational footprinting algorithms have been developed to detect TFBSs by investigating chromatin accessibility patterns, but also have their limitations. To improve on these methods, we have developed a footprinting method to predict Transcription factor footpRints in Active Chromatin Elements (TRACE). Trace incorporates DNase-seq data and PWMs within a multivariate Hidden Markov Model (HMM) to detect footprint-like regions with matching motifs. Trace is an unsupervised method that accurately annotates binding sites for specific TFs automatically with no requirement on pre-generated candidate binding sites or ChIP-seq training data. Compared to published footprinting algorithms, TRACE has the best overall performance with the distinct advantage of targeting multiple motifs in a single model.

scholarly journals Ubx orchestrates tissue identity through regional and bidirectional changes to chromatin accessibility

2021 ◽  
Author(s):  
Ryan Loker ◽  
Jordyn E. Sanner ◽  
Richard S. Mann
Keyword(s):  
Transcription Factor ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Chromatin Accessibility ◽  
Thoracic Segment ◽  
Hox Proteins ◽  
The Third ◽  
Gene Regulatory ◽  
Hox Protein ◽  
Hox Cofactors

AbstractHox proteins are homeodomain transcription factors that diversify serially homologous segments along the animal body axis, as revealed by the classic bithorax phenotype of Drosophila melanogaster where mutations in Ultrabithorax (Ubx) transform the third thoracic segment into the likeness of the second thoracic segment. To specify segment identity we show that Ubx both increases and decreases chromatin accessibility, coinciding with its role as both an activator and repressor of transcription. Surprisingly, whether Ubx functions as an activator or repressor differs depending on the proximal-distal position in the segment and the availability of Hox cofactors. Ubx-mediated changes to chromatin accessibility positively and negatively impact the binding of Scalloped (Sd), a transcription factor that is required for appendage development in both segments. These findings reveal how a single Hox protein can modify complex gene regulatory networks to transform the identity of an entire tissue.

scholarly journals Evolution and multiple roles of the Pancrustacea specific transcription factor zelda in insects

2017 ◽  
Author(s):  
Lupis Ribeiro ◽  
Vitória Tobias-Santos ◽  
Danielle Santos ◽  
Felipe Antunes ◽  
Geórgia Feltran ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Chromatin Accessibility ◽  
Multiple Roles ◽  
Biological Processes ◽  
Regulatory Modules ◽  
Ancestral Function ◽  
Gene Regulatory ◽  
Whole Egg

SummaryGene regulatory networks (GRN) evolve as a result of the coevolutionary process acting on transcription factors and the cis-regulatory modules (CRMs) they bind. The zinc-finger transcription factor (TF) zelda (zld) is essential for maternal zygotic transition (MZT) in Drosophila melanogaster, where it directly binds over thousand CRMs to regulate chromatin accessibility. D. melanogaster displays a long germ type of embryonic development, where all segments are simultaneously generated along the whole egg. However, it remains unclear if zld is also involved in MZT of short-germ insects (including those from basal lineages) or in other biological processes. Here we show that zld is an innovation of the Pancrustacea lineage, being absent in more distant arthropods (e.g. chelicerates) and other organisms. To better understand zld’s ancestral function, we thoroughly investigated its roles in a short-germ beetle, Tribolium castaneum, using molecular biology and computational approaches. Our results demonstrate roles for zld not only during the MZT, but also in posterior segmentation and patterning of imaginal disc derived structures. Further, we also demonstrate that zld is critical for posterior segmentation in the hemipteran Rhodnius prolixus, indicating this function predates the origin of holometabolous insects and was subsequently lost in long-germ insects. Our results unveil new roles of zld in maintaining pluripotent state of progenitor cells at the posterior region and suggest that changes in expression of zld (and probably other pioneer TFs) are critical in the evolution of insect GRNs.

scholarly journals Evolutionary flexibility in flooding response circuitry in angiosperms

Science ◽  
2019 ◽  
Vol 365 (6459) ◽  
pp. 1291-1295 ◽  
Author(s):  
Mauricio A. Reynoso ◽  
Kaisa Kajala ◽  
Marko Bajic ◽  
Donnelly A. West ◽  
Germain Pauluzzi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
High Resolution ◽  
Gene Regulatory Networks ◽  
Wild Species ◽  
Regulatory Networks ◽  
Chromatin Accessibility ◽  
High Resolution Analysis ◽  
Resolution Analysis ◽  
Gene Regulatory

Flooding due to extreme weather threatens crops and ecosystems. To understand variation in gene regulatory networks activated by submergence, we conducted a high-resolution analysis of chromatin accessibility and gene expression at three scales of transcript control in four angiosperms, ranging from a dryland-adapted wild species to a wetland crop. The data define a cohort of conserved submergence-activated genes with signatures of overlapping cis regulation by four transcription factor families. Syntenic genes are more highly expressed than nonsyntenic genes, yet both can have the cis motifs and chromatin accessibility associated with submergence up-regulation. Whereas the flexible circuitry spans the eudicot-monocot divide, the frequency of specific cis motifs, extent of chromatin accessibility, and degree of submergence activation are more prevalent in the wetland crop and may have adaptive importance.

scholarly journals High-throughput single-cell chromatin accessibility CRISPR screens enable unbiased identification of regulatory networks in cancer

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Sarah E. Pierce ◽  
Jeffrey M. Granja ◽  
William J. Greenleaf
Keyword(s):  
Transcription Factor ◽  
Single Cell ◽  
Cancer Cells ◽  
High Throughput ◽  
Regulatory Networks ◽  
Temporal Dynamics ◽  
Chromatin Accessibility ◽  
Regulatory Regions ◽  
Factor Binding ◽  
Genome Wide

AbstractChromatin accessibility profiling can identify putative regulatory regions genome wide; however, pooled single-cell methods for assessing the effects of regulatory perturbations on accessibility are limited. Here, we report a modified droplet-based single-cell ATAC-seq protocol for perturbing and evaluating dynamic single-cell epigenetic states. This method (Spear-ATAC) enables simultaneous read-out of chromatin accessibility profiles and integrated sgRNA spacer sequences from thousands of individual cells at once. Spear-ATAC profiling of 104,592 cells representing 414 sgRNA knock-down populations reveals the temporal dynamics of epigenetic responses to regulatory perturbations in cancer cells and the associations between transcription factor binding profiles.

scholarly journals Modular Evolution of DNA-Binding Preference of a Tbrain Transcription Factor Provides a Mechanism for Modifying Gene Regulatory Networks

2014 ◽  
Vol 31 (10) ◽  
pp. 2672-2688 ◽  
Author(s):  
Alys M. Cheatle Jarvela ◽  
Lisa Brubaker ◽  
Anastasia Vedenko ◽  
Anisha Gupta ◽  
Bruce A. Armitage ◽  
...  
Keyword(s):  
Transcription Factor ◽  
Dna Binding ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Modular Evolution ◽  
Binding Preference ◽  
Gene Regulatory

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.

Sign in / Sign up

Export Citation Format

Share Document