scholarly journals Young transposable elements rewired gene regulatory networks in human and chimpanzee hippocampal intermediate progenitors

2021 ◽  
Author(s):  
Sruti Patoori ◽  
Samantha Barnada ◽  
Marco Trizzino
Keyword(s):  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Hippocampal Volume ◽  
Hippocampal Neurogenesis ◽  
Endogenous Retroviruses ◽  
Brain Functions ◽  
Intermediate Progenitors ◽  
Gene Regulatory ◽  
Species Specific

The hippocampus is associated with essential brain functions such as learning and memory. Human hippocampal volume is significantly greater than expected when compared to non-human apes, suggesting a recent expansion. Intermediate progenitors, which are able to undergo multiple rounds of proliferative division before a final neurogenic division, may have played a role in the evolutionary hippocampal expansion. To investigate the evolution of gene regulatory networks underpinning hippocampal neurogenesis in apes, we leveraged the differentiation of human and chimpanzee induced Pluripotent Stem Cells into TBR2-positive hippocampal intermediate progenitors (hpIPCs). We find that the gene networks active in hpIPCs are significantly different between humans and chimpanzees, with ~2,500 genes differentially expressed. We demonstrate that species-specific transposon-derived enhancers contribute to these transcriptomic differences. Young transposons, predominantly Endogenous Retroviruses (ERVs) and SINE-Vntr-Alus (SVAs), were co-opted as enhancers in a species-specific manner. Human-specific SVAs provided substrates for thousands of novel TBR2 binding sites, and CRISPR-mediated repression of these SVAs attenuates the expression of ~25% of the genes that are upregulated in human intermediate progenitors relative to the same cell population in the chimpanzee.

scholarly journals A primate-specific retroviral enhancer wires the XACT lncRNA into the core pluripotency network in humans

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Miguel Casanova ◽  
Madeleine Moscatelli ◽  
Louis Édouard Chauvière ◽  
Christophe Huret ◽  
Julia Samson ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Endogenous Retroviruses ◽  
X Chromosomes ◽  
Regulatory Pathways ◽  
Gene Regulatory ◽  
Non Coding Rnas ◽  
Specific Regulation ◽  
Species Specific ◽  
And Function

AbstractTransposable elements (TEs) have been proposed to play an important role in driving the expansion of gene regulatory networks during mammalian evolution, notably by contributing to the evolution and function of long non-coding RNAs (lncRNAs). XACT is a primate-specific TE-derived lncRNA that coats active X chromosomes in pluripotent cells and may contribute to species-specific regulation of X-chromosome inactivation. Here we explore how different families of TEs have contributed to shaping the XACT locus and coupling its expression to pluripotency. Through a combination of sequence analysis across primates, transcriptional interference, and genome editing, we identify a critical enhancer for the regulation of the XACT locus that evolved from an ancestral group of mammalian endogenous retroviruses (ERVs), prior to the emergence of XACT. This ERV was hijacked by younger hominoid-specific ERVs that gave rise to the promoter of XACT, thus wiring its expression to the pluripotency network. This work illustrates how retroviral-derived sequences may intervene in species-specific regulatory pathways.

A study of structural properties of gene network graphs for mathematical modeling of integrated mosaic gene networks

2017 ◽  
Vol 15 (02) ◽  
pp. 1650045 ◽  
Author(s):  
Olga V. Petrovskaya ◽  
Evgeny D. Petrovskiy ◽  
Inna N. Lavrik ◽  
Vladimir A. Ivanisenko
Keyword(s):  
Mathematical Modeling ◽  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Gene Network ◽  
Regulatory Networks ◽  
Network Modeling ◽  
Computer Experiments ◽  
Linear Control ◽  
Expression Of Genes ◽  
Gene Regulatory

Gene network modeling is one of the widely used approaches in systems biology. It allows for the study of complex genetic systems function, including so-called mosaic gene networks, which consist of functionally interacting subnetworks. We conducted a study of a mosaic gene networks modeling method based on integration of models of gene subnetworks by linear control functionals. An automatic modeling of 10,000 synthetic mosaic gene regulatory networks was carried out using computer experiments on gene knockdowns/knockouts. Structural analysis of graphs of generated mosaic gene regulatory networks has revealed that the most important factor for building accurate integrated mathematical models, among those analyzed in the study, is data on expression of genes corresponding to the vertices with high properties of centrality.

scholarly journals Universal attenuators and their interactions with feedback loops in gene regulatory networks

2016 ◽  
Author(s):  
Dianbo Liu ◽  
Luca Albergante ◽  
Timothy J Newman
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Human Cancer ◽  
Cancer Cell Line ◽  
Feedback Loops ◽  
Human Cancer Cell ◽  
E Coli ◽  
Gene Regulatory

AbstractUsing a combination of mathematical modelling, statistical simulation and large-scale data analysis we study the properties of linear regulatory chains (LRCs) within gene regulatory networks (GRNs). Our modelling indicates that downstream genes embedded within LRCs are highly insulated from the variation in expression of upstream genes, and thus LRCs act as attenuators. This observation implies a progressively weaker functionality of LRCs as their length increases. When analysing the preponderance of LRCs in the GRNs of E. coli K12 and several other organisms, we find that very long LRCs are essentially absent. In both E. coli and M. tuberculosis we find that four-gene LRCs are intimately linked to identical feedback loops that are involved in potentially chaotic stress response, indicating that the dynamics of these potentially destabilising motifs are strongly restrained under homeostatic conditions. The same relationship is observed in a human cancer cell line (K562), and we postulate that four-gene LRCs act as “universal attenuators”. These findings suggest a role for long LRCs in dampening variation in gene expression, thereby protecting cell identity, and in controlling dramatic shifts in cell-wide gene expression through inhibiting chaos-generating motifs.In briefWe present a general principle that linear regulatory chains exponentially attenuate the range of expression in gene regulatory networks. The discovery of a universal interplay between linear regulatory chains and genetic feedback loops in microorganisms and a human cancer cell line is analysed and discussed.HighlightsWithin gene networks, linear regulatory chains act as exponentially strong attenuators of upstream variationBecause of their exponential behaviour, linear regulatory chains beyond a few genes provide no additional functionality and are rarely observed in gene networks across a range of different organismsNovel interactions between four-gene linear regulatory chains and feedback loops were discovered in E. coli, M. tuberculosis and human cancer cells, suggesting a universal mechanism of control.

scholarly journals CoGNaC: A Chaste Plugin for the Multiscale Simulation of Gene Regulatory Networks Driving the Spatial Dynamics of Tissues and Cancer

2015 ◽  
Vol 14s4 ◽  
pp. CIN.S19965 ◽  
Author(s):  
Simone Rubinacci ◽  
Alex Graudenzi ◽  
Giulio Caravagna ◽  
Giancarlo Mauri ◽  
James Osborne ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Dynamical Behavior ◽  
Gene Activation ◽  
Spatial Dynamics ◽  
Multiscale Simulation ◽  
Activation Patterns ◽  
Gene Regulatory ◽  
Cellular Phenotypes

We introduce a Chaste plugin for the generation and the simulation of Gene Regulatory Networks (GRNs) in multiscale models of multicellular systems. Chaste is a widely used and versatile computational framework for the multiscale modeling and simulation of multicellular biological systems. The plugin, named CoGNaC (Chaste and Gene Networks for Cancer), allows the linking of the regulatory dynamics to key properties of the cell cycle and of the differentiation process in populations of cells, which can subsequently be modeled using different spatial modeling scenarios. The approach of CoGNaC focuses on the emergent dynamical behavior of gene networks, in terms of gene activation patterns characterizing the different cellular phenotypes of real cells and, especially, on the overall robustness to perturbations and biological noise. The integration of this approach within Chaste's modular simulation framework provides a powerful tool to model multicellular systems, possibly allowing for the formulation of novel hypotheses on gene regulation, cell differentiation, and, in particular, cancer emergence and development. In order to demonstrate the usefulness of CoGNaC over a range of modeling paradigms, two example applications are presented. The first of these concerns the characterization of the gene activation patterns of human T-helper cells. The second example is a multiscale simulation of a simplified intestinal crypt, in which, given certain conditions, tumor cells can emerge and colonize the tissue.

Using Bayesian Networks to Construct Gene Regulatory Networks from Microarray Data

2012 ◽  
pp. 1-6
Author(s):  
Ai Kung Tan ◽  
Mohd Saberi Mohamad
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Cell Cycle ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Cell Cycle Gene ◽  
Gene Regulatory ◽  
Cell Cycle Gene Expression

In this research, Bayesian network is proposed as the model to construct gene regulatory networks from Saccharomyces cerevisiae cell-cycle gene expression dataset and Escherichia coli dataset due to its capability of handling microarray datasets with missing values. The goal of this research is to study and to understand the framework of the Bayesian networks, and then to construct gene regulatory networks from Saccharomyces cerevisiae cell-cycle gene expression dataset and Escherichia coli dataset by developing Bayesian networks using hill-climbing algorithm and Efron’s bootstrap approach and then the performance of the constructed gene networks of Saccharomyces cerevisiae are evaluated and are compared with the previously constructed sub-networks by Dejori [14]. At the end of this research, the gene networks constructed for Saccharomyces cerevisiae not only have achieved high True Positive Rate (more than 90%), but the networks constructed also have discovered more potential interactions between genes. Therefore, it can be concluded that the performance of the gene regulatory networks constructed using Bayesian networks in this research is proved to be better because it can reveal more gene relationships. Dalam penyelidikan ini, Bayesian network adalah dicadangkan sebagai model untuk membina gene regulatory networks dari kitar sel S. cerevisiae set data disebabkan keupayaannya untuk mengendali set data microarray yang mempunyai nilai-nilai yang hilang. Tujuan penyelidikan ini adalah untuk mempelajari dan memahami rekabentuk untuk Bayesian network, dan kemudian untuk membina gene regulatory networks dari data Saccharomyces cerevisiae cell-cycle gene expression dan data Escherichia coli dengan membina model Bayesian networks dengan menggunakan algoritma hill-climbing serta Efron’s bootstrap approach dan gene networks yang dibina untuk Saccharomyces cerevisiae dibandingkan dengan sub-networks yang dibina oleh Dejori [14]. Pada akhir kajian ini, gene networks yang dibina untuk Saccharomyces cerevisiae bukan sahaja telah mencapai True Positive Rate yang tinggi (lebih dari 90%), tetapi gene networks yang dibina juga telah menemui lebih banyak interaksi berpotensi antara gen. Oleh kerana itu, dapat disimpulkan bahawa prestasi gene networks yang dibina menggunakan Bayesian network dalam kajian ini adalah terbukti lebih baik kerana ia boleh mendedahkan lebih banyak hubungan antara gen.

scholarly journals Gene regulatory networks controlling vertebrate retinal regeneration

Science ◽  
2020 ◽  
Vol 370 (6519) ◽  
pp. eabb8598 ◽  
Author(s):  
Thanh Hoang ◽  
Jie Wang ◽  
Patrick Boyd ◽  
Fang Wang ◽  
Clayton Santiago ◽  
...  
Keyword(s):  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Chromatin Accessibility ◽  
Specific Gene ◽  
Muller Glia ◽  
Müller Glia ◽  
Retinal Regeneration ◽  
Adult Mice ◽  
Gene Regulatory

Injury induces retinal Müller glia of certain cold-blooded vertebrates, but not those of mammals, to regenerate neurons. To identify gene regulatory networks that reprogram Müller glia into progenitor cells, we profiled changes in gene expression and chromatin accessibility in Müller glia from zebrafish, chick, and mice in response to different stimuli. We identified evolutionarily conserved and species-specific gene networks controlling glial quiescence, reactivity, and neurogenesis. In zebrafish and chick, the transition from quiescence to reactivity is essential for retinal regeneration, whereas in mice, a dedicated network suppresses neurogenic competence and restores quiescence. Disruption of nuclear factor I transcription factors, which maintain and restore quiescence, induces Müller glia to proliferate and generate neurons in adult mice after injury. These findings may aid in designing therapies to restore retinal neurons lost to degenerative diseases.

INFERENCE OF LARGE-SCALE GENE REGULATORY NETWORKS USING REGRESSION-BASED NETWORK APPROACH

2009 ◽  
Vol 07 (04) ◽  
pp. 717-735 ◽  
Author(s):  
HASEONG KIM ◽  
JAE K. LEE ◽  
TAESUNG PARK
Keyword(s):  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Large Scale ◽  
Simple Procedure ◽  
Simulated Data ◽  
Computation Time ◽  
Network Approach ◽  
Global Regulators ◽  
Gene Regulatory

The gene regulatory network modeling plays a key role in search for relationships among genes. Many modeling approaches have been introduced to find the causal relationship between genes using time series microarray data. However, they have been suffering from high dimensionality, overfitting, and heavy computation time. Further, the selection of a best model among several possible competing models is not guaranteed that it is the best one. In this study, we propose a simple procedure for constructing large scale gene regulatory networks using a regression-based network approach. We determine the optimal out-degree of network structure by using the sum of squared coefficients which are obtained from all appropriate regression models. Through the simulated data, accuracy of estimation and robustness against noise are computed in order to compare with the vector autoregressive regression model. Our method shows high accuracy and robustness for inferring large-scale gene networks. Also it is applied to Caulobacter crecentus cell cycle data consisting of 1472 genes. It shows that many genes are regulated by two transcription factors, ctrA and gcrA, that are known for global regulators.

scholarly journals Reverse Engineering Gene Networks Using Global-Local Shrinkage Rules

2019 ◽  
Author(s):  
Viral Panchal ◽  
Daniel Linder
Keyword(s):  
T Cell Activation ◽  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Cell Activation ◽  
Network Inference ◽  
Structure Learning ◽  
Multiple Scales ◽  
Real Data ◽  
Gene Regulatory

AbstractInferring gene regulatory networks from high-throughput ‘omics’ data has proven to be a computationally demanding task of critical importance. Frequently the classical methods breakdown due to the curse of dimensionality, and popular strategies to overcome this are typically based on regularized versions of the classical methods. However, these approaches rely on loss functions that may not be robust and usually do not allow for the incorporation of prior information in a straightforward way. Fully Bayesian methods are equipped to handle both of these shortcomings quite naturally, and they offer potential for improvements in network structure learning. We propose a Bayesian hierarchical model to reconstruct gene regulatory networks from time series gene expression data, such as those common in perturbation experiments of biological systems. The proposed methodology utilizes global-local shrinkage priors for posterior selection of regulatory edges and relaxes the common normal likelihood assumption in order to allow for heavy-tailed data, which was shown in several of the cited references to severely impact network inference. We provide a sufficient condition for posterior propriety and derive an efficient MCMC via Gibbs sampling in the Appendix. We describe a novel way to detect multiple scales based on the corresponding posterior quantities. Finally, we demonstrate the performance of our approach in a simulation study and compare it with existing methods on real data from a T-cell activation study.

scholarly journals Reverse engineering gene networks using global–local shrinkage rules

2019 ◽  
Vol 10 (1) ◽  
pp. 20190049 ◽  
Author(s):  
Viral Panchal ◽  
Daniel F. Linder
Keyword(s):  
T Cell Activation ◽  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Cell Activation ◽  
Network Inference ◽  
Structure Learning ◽  
Multiple Scales ◽  
Real Data ◽  
Gene Regulatory

Inferring gene regulatory networks from high-throughput ‘omics’ data has proven to be a computationally demanding task of critical importance. Frequently, the classical methods break down owing to the curse of dimensionality, and popular strategies to overcome this are typically based on regularized versions of the classical methods. However, these approaches rely on loss functions that may not be robust and usually do not allow for the incorporation of prior information in a straightforward way. Fully Bayesian methods are equipped to handle both of these shortcomings quite naturally, and they offer the potential for improvements in network structure learning. We propose a Bayesian hierarchical model to reconstruct gene regulatory networks from time-series gene expression data, such as those common in perturbation experiments of biological systems. The proposed methodology uses global–local shrinkage priors for posterior selection of regulatory edges and relaxes the common normal likelihood assumption in order to allow for heavy-tailed data, which were shown in several of the cited references to severely impact network inference. We provide a sufficient condition for posterior propriety and derive an efficient Markov chain Monte Carlo via Gibbs sampling in the electronic supplementary material. We describe a novel way to detect multiple scales based on the corresponding posterior quantities. Finally, we demonstrate the performance of our approach in a simulation study and compare it with existing methods on real data from a T-cell activation study.

Methods for Reverse Engineering of Gene Regulatory Networks

2009 ◽  
pp. 497-515
Author(s):  
Hendrik Hache
Keyword(s):  
Experimental Data ◽  
Reverse Engineering ◽  
Computational Methods ◽  
High Throughput ◽  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Regulatory Networks ◽  
Gene Regulatory

In this chapter, different methods and applications for reverse engineering of gene regulatory networks that have been developed in recent years are discussed and compared. Inferring gene networks from different kinds of experimental data are a challenging task that emerged, especially with the development of high throughput technologies. Various computational methods based on diverse principles were introduced to identify new regulations among genes. Mathematical aspects of the models are highlighted, and applications for reverse engineering are mentioned.

Sign in / Sign up

Export Citation Format

Share Document