scholarly journals The impact of whole genome duplications on the human gene regulatory networks

2021 ◽  
Vol 17 (12) ◽  
pp. e1009638
Author(s):  
Francesco Mottes ◽  
Chiara Villa ◽  
Matteo Osella ◽  
Michele Caselle
Keyword(s):  
Gene Regulatory Networks ◽  
Protein Interactions ◽  
Regulatory Networks ◽  
Human Gene ◽  
Small Scale ◽  
Whole Genome ◽  
Mirna Regulation ◽  
Vertebrate Lineage ◽  
Gene Regulatory ◽  
The Impact

This work studies the effects of the two rounds of Whole Genome Duplication (WGD) at the origin of the vertebrate lineage on the architecture of the human gene regulatory networks. We integrate information on transcriptional regulation, miRNA regulation, and protein-protein interactions to comparatively analyse the role of WGD and Small Scale Duplications (SSD) in the structural properties of the resulting multilayer network. We show that complex network motifs, such as combinations of feed-forward loops and bifan arrays, deriving from WGD events are specifically enriched in the network. Pairs of WGD-derived proteins display a strong tendency to interact both with each other and with common partners and WGD-derived transcription factors play a prominent role in the retention of a strong regulatory redundancy. Combinatorial regulation and synergy between different regulatory layers are in general enhanced by duplication events, but the two types of duplications contribute in different ways. Overall, our findings suggest that the two WGD events played a substantial role in increasing the multi-layer complexity of the vertebrate regulatory network by enhancing its combinatorial organization, with potential consequences on its overall robustness and ability to perform high-level functions like signal integration and noise control. Lastly, we discuss in detail the RAR/RXR pathway as an illustrative example of the evolutionary impact of WGD duplications in human.

scholarly journals The impact of whole genome duplications on the human gene regulatory networks

2021 ◽  
Author(s):  
Francesco Mottes ◽  
Chiara Villa ◽  
Matteo Osella ◽  
Michele Caselle
Keyword(s):  
Gene Regulatory Networks ◽  
Protein Interactions ◽  
Regulatory Networks ◽  
Human Gene ◽  
Small Scale ◽  
Whole Genome ◽  
Mirna Regulation ◽  
Vertebrate Lineage ◽  
Gene Regulatory ◽  
The Impact

This work studies the effects of the two rounds of Whole Genome Duplication (WGD) at the origin of the vertebrate lineage on the architecture of the human gene regulatory networks. We integrate information on transcriptional regulation, miRNA regulation, and protein-protein interactions to comparatively analyse the role of WGD and Small Scale Duplications (SSD) in the structural properties of the resulting multilayer network. We show that complex network motifs, such as combinations of feed-forward loops and bifan arrays, deriving from WGD events are specifically enriched in the network. Pairs of WGD-derived proteins display a strong tendency to interact both with each other and with common partners and WGD-derived transcription factors play a prominent role in the retention of a strong regulatory redundancy. Combinatorial regulation and synergy between different regulatory layers are in general enhanced by duplication events, but the two types of duplications contribute in different ways. Overall, our findings suggest that the two WGD events played a substantial role in increasing the multi-layer complexity of the vertebrate regulatory network by enhancing its combinatorial organization, with potential consequences on its overall robustness and ability to perform high-level functions like signal integration and noise control.

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 Controlling Robots with Fractal Gene Regulatory Networks

2005 ◽  
pp. 320-339 ◽  
Author(s):  
Peter J. Bentley
Keyword(s):  
Gene Regulatory Networks ◽  
Protein Interactions ◽  
Regulatory Networks ◽  
Developmental Process ◽  
Mandelbrot Set ◽  
Gene Regulatory ◽  
Robot Path

Fractal proteins are a new evolvable method of mapping genotype to phenotype through a developmental process, where genes are expressed into proteins comprised of subsets of the Mandelbrot set. The resulting network of gene and protein interactions can be designed by evolution to produce specific patterns, which in turn can be used to solve problems. This chapter introduces the fractal development algorithm in detail and describes the use of fractal gene regulatory networks for learning a robot path through a series of obstacles. The results indicate the ability of this system to learn regularities in solutions and automatically create and use modules.

scholarly journals Stochastic resonances in a distributed genetic broadcasting system: the NF κ B/I κ B paradigm

2018 ◽  
Vol 15 (138) ◽  
pp. 20170809 ◽  
Author(s):  
Zhipeng Wang ◽  
Davit A. Potoyan ◽  
Peter G. Wolynes
Keyword(s):  
Gene Regulatory Networks ◽  
Binding Sites ◽  
Regulatory Networks ◽  
Stochastic Dynamics ◽  
Extracellular Signals ◽  
Broadcasting System ◽  
Gene Regulatory ◽  
And Performance ◽  
The Impact ◽  
Kinetic Regulation

Gene regulatory networks must relay information from extracellular signals to downstream genes in an efficient, timely and coherent manner. Many complex functional tasks such as the immune response require system-wide broadcasting of information not to one but to many genes carrying out distinct functions whose dynamical binding and unbinding characteristics are widely distributed. In such broadcasting networks, the intended target sites are also often dwarfed in number by the even more numerous non-functional binding sites. Taking the genetic regulatory network of NF κ B as an exemplary system we explore the impact of having numerous distributed sites on the stochastic dynamics of oscillatory broadcasting genetic networks pointing out how resonances in binding cycles control the network's specificity and performance. We also show that active kinetic regulation of binding and unbinding through molecular stripping of DNA bound transcription factors can lead to a higher coherence of gene-co-expression and synchronous clearance.

scholarly journals Automated Identification of Core Regulatory Genes in Human Gene Regulatory Networks

2015 ◽  
Vol 11 (9) ◽  
pp. e1004504 ◽  
Author(s):  
Vipin Narang ◽  
Muhamad Azfar Ramli ◽  
Amit Singhal ◽  
Pavanish Kumar ◽  
Gennaro de Libero ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Human Gene ◽  
Regulatory Genes ◽  
Automated Identification ◽  
Gene Regulatory

scholarly journals Predicting genotype-specific gene regulatory networks

2021 ◽  
Author(s):  
Deborah Weighill ◽  
Marouen Ben Guebila ◽  
Kimberly Glass ◽  
John Quackenbush ◽  
John Platig
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Gene Regulatory Networks ◽  
Genetic Variants ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Specific Gene ◽  
Disease Etiology ◽  
Gene Regulatory ◽  
The Impact

AbstractThe majority of disease-associated genetic variants are thought to have regulatory effects, including the disruption of transcription factor (TF) binding and the alteration of downstream gene expression. Identifying how a person’s genotype affects their individual gene regulatory network has the potential to provide important insights into disease etiology and to enable improved genotype-specific disease risk assessments and treatments. However, the impact of genetic variants is generally not considered when constructing gene regulatory networks. To address this unmet need, we developed EGRET (Estimating the Genetic Regulatory Effect on TFs), which infers a genotype-specific gene regulatory network (GRN) for each individual in a study population by using message passing to integrate genotype-informed TF motif predictions - derived from individual genotype data, the predicted effects of variants on TF binding and gene expression, and TF motif predictions - with TF protein-protein interactions and gene expression. Comparing EGRET networks for two blood-derived cell lines identified genotype-associated cell-line specific regulatory differences which were subsequently validated using allele-specific expression, chromatin accessibility QTLs, and differential TF binding from ChIP-seq. In addition, EGRET GRNs for three cell types across 119 individuals captured regulatory differences associated with disease in a cell-type-specific manner. Our analyses demonstrate that EGRET networks can capture the impact of genetic variants on complex phenotypes, supporting a novel fine-scale stratification of individuals based on their genetic background. EGRET is available through the Network Zoo R package (netZooR v0.9; netzoo.github.io).

scholarly journals Cell-free gene-regulatory network engineering with synthetic transcription factors

2019 ◽  
Vol 116 (13) ◽  
pp. 5892-5901 ◽  
Author(s):  
Zoe Swank ◽  
Nadanai Laohakunakorn ◽  
Sebastian J. Maerkl
Keyword(s):  
Transcription Factors ◽  
High Throughput ◽  
Gene Regulatory Networks ◽  
Protein Interactions ◽  
Transcriptional Repression ◽  
Regulatory Networks ◽  
De Novo ◽  
Mechanistic Modeling ◽  
Free System ◽  
Gene Regulatory

Gene-regulatory networks are ubiquitous in nature and critical for bottom-up engineering of synthetic networks. Transcriptional repression is a fundamental function that can be tuned at the level of DNA, protein, and cooperative protein–protein interactions, necessitating high-throughput experimental approaches for in-depth characterization. Here, we used a cell-free system in combination with a high-throughput microfluidic device to comprehensively study the different tuning mechanisms of a synthetic zinc-finger repressor library, whose affinity and cooperativity can be rationally engineered. The device is integrated into a comprehensive workflow that includes determination of transcription-factor binding-energy landscapes and mechanistic modeling, enabling us to generate a library of well-characterized synthetic transcription factors and corresponding promoters, which we then used to build gene-regulatory networks de novo. The well-characterized synthetic parts and insights gained should be useful for rationally engineering gene-regulatory networks and for studying the biophysics of transcriptional regulation.

scholarly journals Distinct Expression and Methylation Patterns for Genes with Different Fates following a Single Whole-Genome Duplication in Flowering Plants

2020 ◽  
Vol 37 (8) ◽  
pp. 2394-2413 ◽  
Author(s):  
Tao Shi ◽  
Razgar Seyed Rahmani ◽  
Paul F Gugger ◽  
Muhua Wang ◽  
Hui Li ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Flowering Plants ◽  
Expression Level ◽  
Small Scale ◽  
Whole Genome ◽  
Functional Constraints ◽  
Expression Divergence ◽  
Protein Protein Interactions ◽  
Methylation Patterns ◽  
The Impact

Abstract For most sequenced flowering plants, multiple whole-genome duplications (WGDs) are found. Duplicated genes following WGD often have different fates that can quickly disappear again, be retained for long(er) periods, or subsequently undergo small-scale duplications. However, how different expression, epigenetic regulation, and functional constraints are associated with these different gene fates following a WGD still requires further investigation due to successive WGDs in angiosperms complicating the gene trajectories. In this study, we investigate lotus (Nelumbo nucifera), an angiosperm with a single WGD during the K–pg boundary. Based on improved intraspecific-synteny identification by a chromosome-level assembly, transcriptome, and bisulfite sequencing, we explore not only the fundamental distinctions in genomic features, expression, and methylation patterns of genes with different fates after a WGD but also the factors that shape post-WGD expression divergence and expression bias between duplicates. We found that after a WGD genes that returned to single copies show the highest levels and breadth of expression, gene body methylation, and intron numbers, whereas the long-retained duplicates exhibit the highest degrees of protein–protein interactions and protein lengths and the lowest methylation in gene flanking regions. For those long-retained duplicate pairs, the degree of expression divergence correlates with their sequence divergence, degree in protein–protein interactions, and expression level, whereas their biases in expression level reflecting subgenome dominance are associated with the bias of subgenome fractionation. Overall, our study on the paleopolyploid nature of lotus highlights the impact of different functional constraints on gene fate and duplicate divergence following a single WGD in plant.

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