Bioinformatics Analysis of Choriocarcinoma-Related MicroRNA-Transcription Factor-Target Gene Regulatory Networks and Validation of Key miRNAs

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.

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Comparative analysis of the transcription-factor gene regulatory networks of E. coli and S. cerevisiae

BMC Systems Biology ◽

10.1186/1752-0509-2-13 ◽

2008 ◽

Vol 2 (1) ◽

Cited By ~ 14

Author(s):

Lev Guzmán-Vargas ◽

Moisés Santillán

Keyword(s):

Transcription Factor ◽

Comparative Analysis ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Transcription Factor Gene ◽

Factor Gene ◽

E Coli ◽

Gene Regulatory

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MicroRNA profiling reveals dysregulated microRNAs and their target gene regulatory networks in cemento-ossifying fibroma

Journal of Oral Pathology and Medicine ◽

10.1111/jop.12650 ◽

2017 ◽

Vol 47 (1) ◽

pp. 78-85 ◽

Cited By ~ 6

Author(s):

Thaís dos Santos Fontes Pereira ◽

João Artur Ricieri Brito ◽

André Luiz Sena Guimarães ◽

Carolina Cavaliéri Gomes ◽

Júlio Cesar Tanos de Lacerda ◽

...

Keyword(s):

Gene Regulatory Networks ◽

Regulatory Networks ◽

Target Gene ◽

Ossifying Fibroma ◽

Microrna Profiling ◽

Gene Regulatory

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Accurate differential analysis of transcription factor activity from gene expression

10.1101/296210 ◽

2018 ◽

Author(s):

Viren Amin ◽

Murat Can Cobanoglu

Keyword(s):

Gene Expression ◽

Transcription Factor ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Expression Data ◽

Differential Analysis ◽

Gene Regulatory ◽

Context Specific ◽

Tf Gene ◽

Perturbation Estimation

AbstractWe present EPEE (Effector and Perturbation Estimation Engine), a method for differential analysis of transcription factor (TF) activity from gene expression data. EPEE addresses two principal challenges in the field, namely incorporating context-specific TF-gene regulatory networks, and accounting for the fact that TF activity inference is intrinsically coupled for all TFs that share targets. Our validations in well-studied immune and cancer contexts show that addressing the overlap challenge and using state-of-the-art regulatory networks enable EPEE to consistently produce accurate results. (Accessible at: https://github.com/Cobanoglu-Lab/EPEE)

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Gene regulatory networks and transcription factor transcriptomics

New Biotechnology ◽

10.1016/j.nbt.2009.06.871 ◽

2009 ◽

Vol 25 ◽

pp. S318

Author(s):

B. Mueller-Roeber ◽

S. Arvidsson ◽

S. Balazadeh ◽

L.G.G. Corrêa ◽

P. Pérez-Rodríguez ◽

...

Keyword(s):

Transcription Factor ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Gene Regulatory

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Constructing Logical Models of Gene Regulatory Networks by Integrating Transcription Factor–DNA Interactions with Expression Data: An Entropy-Based Approach

Journal of Computational Biology ◽

10.1089/cmb.2011.0100 ◽

2012 ◽

Vol 19 (1) ◽

pp. 30-41 ◽

Cited By ~ 20

Author(s):

Guy Karlebach ◽

Ron Shamir

Keyword(s):

Transcription Factor ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Expression Data ◽

Dna Interactions ◽

Logical Models ◽

Gene Regulatory

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Inductive Inference of Gene Regulatory Network Using Supervised and Semi-supervised Graph Neural Networks

10.1101/2020.09.27.315382 ◽

2020 ◽

Author(s):

Juexin Wang ◽

Anjun Ma ◽

Qin Ma ◽

Dong Xu ◽

Trupti Joshi

Keyword(s):

Neural Network ◽

Gene Expression ◽

Gene Expression Data ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Target Gene ◽

Graph Embedding ◽

Expression Data ◽

Graph Classification ◽

Gene Regulatory

AbstractDiscovering gene regulatory relationships and reconstructing gene regulatory networks (GRN) based on gene expression data is a classical, long-standing computational challenge in bioinformatics. Computationally inferring a possible regulatory relationship between two genes can be formulated as a link prediction problem between two nodes in a graph. Graph neural network (GNN) provides an opportunity to construct GRN by integrating topological neighbor propagation through the whole gene network. We propose an end-to-end gene regulatory graph neural network (GRGNN) approach to reconstruct GRNs from scratch utilizing the gene expression data, in both a supervised and a semi-supervised framework. To get better inductive generalization capability, GRN inference is formulated as a graph classification problem, to distinguish whether a subgraph centered at two nodes contains the link between the two nodes. A linked pair between a transcription factor (TF) and a target gene, and their neighbors are labeled as a positive subgraph, while an unlinked TF and target gene pair and their neighbors are labeled as a negative subgraph. A GNN model is constructed with node features from both explicit gene expression and graph embedding. We demonstrate a noisy starting graph structure built from partial information, such as Pearson’s correlation coefficient and mutual information can help guide the GRN inference through an appropriate ensemble technique. Furthermore, a semi-supervised scheme is implemented to increase the quality of the classifier. When compared with established methods, GRGNN achieved state-of-the-art performance on the DREAM5 GRN inference benchmarks. GRGNN is publicly available at https://github.com/juexinwang/GRGNN.HighlightsWe present a novel formulation of graph classification in inferring gene regulatory relationships from gene expression and graph embedding.Our method leverages a powerful framework, gene regulatory graph neural network (GRGNN), which is flexible and powerful to ensemble statistical powers from a number of heuristic skeletons.Our results show GRGRNN outperforms previous supervised and unsupervised methods inductively on benchmarks.GRGNN can be interpreted and explained following the biological network motif hypothesis in gene regulatory networks.

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Modeling Transcriptional Regulation Using Gene Regulatory Networks Based on Multi-Omics Data Sources

10.21203/rs.3.rs-112300/v1 ◽

2020 ◽

Author(s):

Neel Patel ◽

William 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 BackgroundTranscriptional 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. ResultsWe 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 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 SKAT(sequence kernel association test) analyses of gene expression. ConclusionsOur models generate refined effect estimates for individual transcription factors, allow characterization of their roles across the genome, and provide a framework for integrating multiple data types into a single model of transcriptional regulation.

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Common ancestry of heterodimerizing TALE homeobox transcription factors across Metazoa and Archaeplastida

10.1101/389700 ◽

2018 ◽

Author(s):

Sunjoo Joo ◽

Ming Hsiu Wang ◽

Gary Lui ◽

Jenny Lee ◽

Andrew Barnas ◽

...

Keyword(s):

Transcription Factors ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Bioinformatics Analysis ◽

Land Plants ◽

Protein Protein Interaction ◽

Functional Conservation ◽

Developmental Mechanisms ◽

Gene Regulatory ◽

Homeobox Transcription Factors

AbstractHomeobox transcription factors (TFs) in the TALE superclass are deeply embedded in the gene regulatory networks that orchestrate embryogenesis. Knotted-like homeobox (KNOX) TFs, homologous to animal MEIS, have been found to drive the haploid-to-diploid transition in both unicellular green algae and land plants via heterodimerization with other TALE superclass TFs, representing remarkable functional conservation of a developmental TF across lineages that diverged one billion years ago. To delineate the ancestry of TALE-TALE heterodimerization, we analyzed TALE endowment in the algal radiations of Archaeplastida, ancestral to land plants. Homeodomain phylogeny and bioinformatics analysis partitioned TALEs into two broad groups, KNOX and non-KNOX. Each group shares previously defined heterodimerization domains, plant KNOX-homology in the KNOX group and animal PBC-homology in the non-KNOX group, indicating their deep ancestry. Protein-protein interaction experiments showed that the TALEs in the two groups all participated in heterodimerization. These results indicate that the TF dyads consisting of KNOX/MEIS and PBC-containing TALEs must have evolved early in eukaryotic evolution, a likely function being to accurately execute the haploid-to-diploid transitions during sexual development.Author summaryComplex multicellularity requires elaborate developmental mechanisms, often based on the versatility of heterodimeric transcription factor (TF) interactions. Highly conserved TALE-superclass homeobox TF networks in major eukaryotic lineages suggest deep ancestry of developmental mechanisms. Our results support the hypothesis that in early eukaryotes, the TALE heterodimeric configuration provided transcription-on switches via dimerization-dependent subcellular localization, ensuring execution of the haploid-to-diploid transition only when the gamete fusion is correctly executed between appropriate partner gametes, a system that then diversified in the several lineages that engage in complex multicellular organization.

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