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

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.

2020 ◽  
Author(s):  
Abhijeet Rajendra Sonawane ◽  
Dawn L. DeMeo ◽  
John Quackenbush ◽  
Kimberly Glass

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.


Author(s):  
Liesbeth Minnoye ◽  
Ibrahim Ihsan Taskiran ◽  
David Mauduit ◽  
Maurizio Fazio ◽  
Linde Van Aerschot ◽  
...  

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.


2020 ◽  
Author(s):  
Liliang Yang ◽  
Kaizhen Wang ◽  
Wenjing Guo ◽  
Xian Chen ◽  
Qinglong Guo ◽  
...  

Abstract Background:RNA polymerase II subunit K (POLR2K) belongs to one of the multiple subunits of RNA polymerase II (Pol II), whose biological function is to synthesize mRNA. Aberrant POLR2K expression is related to carcinogenesis. However, POLR2K’s underlying role in bladder cancer has not been explored. In the current study, we intend to analyze the function of POLR2K and its regulatory network within bladder cancer.Methods: Public sequencing data was obtained from GEO and TCGA to investigate POLR2K expression and regulatory network within bladder cancer (BLCA) by using GEPIA and Oncomine as well as cBioPortal online tool. LinkedOmics was employed to identify genes displaying significantly differential expression patterns and to perform GO and KEGG analyses. After differential genes was assigned and ranked, GSEA analyses was performed to obtain target networks for transcription factors, miRNAs, and kinases that could regulate POLR2K–associated gene network. Subsequent functional webwork analyses were used to identify cancer-relevant pathways Moreover, POLR2K gene is verified, by ChIP-seq in MCF-7 cell line , with transcription factor binding evidence in the ENCODE Transcription Factor Binding Site Profiles dataset.Conclusions: The current study implies that POLR2K gene is overexpressed and often amplified in BLCA, providing the first evidence that POLR2K deregulation, in particular increased transcription, may promote BLCA. These findings uncover a unique expression patterns of POLR2K and its potential regulatory networks in BLCA, contributing greatly to study of the role of POLR2K in cancer development.


2019 ◽  
Author(s):  
Christopher A Jackson ◽  
Dayanne M Castro ◽  
Giuseppe-Antonio Saldi ◽  
Richard Bonneau ◽  
David Gresham

AbstractUnderstanding how gene expression programs are controlled requires identifying regulatory relationships between transcription factors and target genes. Gene regulatory networks are typically constructed from gene expression data acquired following genetic perturbation or environmental stimulus. Single-cell RNA sequencing (scRNAseq) captures the gene expression state of thousands of individual cells in a single experiment, offering advantages in combinatorial experimental design, large numbers of independent measurements, and accessing the interaction between the cell cycle and environmental responses that is hidden by population-level analysis of gene expression. To leverage these advantages, we developed a method for transcriptionally barcoding gene deletion mutants and performing scRNAseq in budding yeast (Saccharomyces cerevisiae). We pooled diverse genotypes in 11 different environmental conditions and determined their expression state by sequencing 38,285 individual cells. We developed, and benchmarked, a framework for learning gene regulatory networks from scRNAseq data that incorporates multitask learning and constructed a global gene regulatory network comprising 12,018 interactions. Our study establishes a general approach to gene regulatory network reconstruction from scRNAseq data that can be employed in any organism.


2014 ◽  
Vol 31 (10) ◽  
pp. 2672-2688 ◽  
Author(s):  
Alys M. Cheatle Jarvela ◽  
Lisa Brubaker ◽  
Anastasia Vedenko ◽  
Anisha Gupta ◽  
Bruce A. Armitage ◽  
...  

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Neel Patel ◽  
William S. Bush

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.


RSC Advances ◽  
2017 ◽  
Vol 7 (37) ◽  
pp. 23222-23233 ◽  
Author(s):  
Wei Liu ◽  
Wen Zhu ◽  
Bo Liao ◽  
Haowen Chen ◽  
Siqi Ren ◽  
...  

Inferring gene regulatory networks from expression data is a central problem in systems biology.


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