Gene regulatory networks controlling vertebrate retinal regeneration

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.

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Cross-species transcriptomic and epigenomic analysis reveals key regulators of injury response and neuronal regeneration in vertebrate retinas

10.1101/717876 ◽

2019 ◽

Cited By ~ 6

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 ◽

Rna Seq ◽

Adult Mice ◽

Gene Regulatory

AbstractInjury induces retinal Müller glia of cold-blooded, but not mammalian, vertebrates to regenerate neurons. To identify gene regulatory networks that control neuronal reprogramming in retinal glia, we comprehensively profiled injury-dependent changes in gene expression and chromatin accessibility in Müller glia from zebrafish, chick and mice using bulk RNA-Seq and ATAC-Seq, as well as single-cell RNA-Seq. Cross-species integrative analysis of these data, together with functional validation, identified evolutionarily conserved and species-specific gene networks controlling glial quiescence, gliosis and neurogenesis. In zebrafish and chick, transition from the resting state to gliosis is essential for initiation of retinal regeneration, while in mice a dedicated network suppresses neurogenic competence and restores quiescence. Selective disruption of NFI family transcription factors, which maintain and restore quiescence, enables Müller glia to proliferate and generate neurons in adult mice following retinal injury. These findings may aid in the design of cell-based therapies aimed at restoring retinal neurons lost to degenerative disease.Summary sentenceThis study identifies gene regulatory networks controlling proliferative and neurogenic competence in retinal Müller glia.

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Automated generation of context-specific Gene Regulatory Networks with a weighted approach in D. melanogaster

10.1101/2020.08.03.232959 ◽

2020 ◽

Author(s):

Leandro Murgas ◽

Sebastian Contreras-Riquelme ◽

J. Eduardo Martínez ◽

Camilo Villaman ◽

Rodrigo Santibáñez ◽

...

Keyword(s):

Gene Expression ◽

Transcription Factors ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Current Knowledge ◽

Regulation Of Gene Expression ◽

Chromatin Accessibility ◽

Reference Network ◽

Specific Gene ◽

Gene Regulatory

AbstractMotivationThe regulation of gene expression is a key factor in the development and maintenance of life in all organisms. This process is carried out mainly through the action of transcription factors (TFs), although other actors such as ncRNAs are involved. In this work, we propose a new method to construct Gene Regulatory Networks (GRNs) depicting regulatory events in a certain context for Drosophila melanogaster. Our approach is based on known relationships between epigenetics and the activity of transcription factors.ResultsWe developed method, Tool for Weighted Epigenomic Networks in D. melanogaster (Fly T-WEoN), which generates GRNs starting from a reference network that contains all known gene regulations in the fly. Regulations that are unlikely taking place are removed by applying a series of knowledge-based filters. Each of these filters is implemented as an independent module that considers a type of experimental evidence, including DNA methylation, chromatin accessibility, histone modifications, and gene expression. Fly T-WEoN is based on heuristic rules that reflect current knowledge on gene regulation in D. melanogaster obtained from literature. Experimental data files can be generated with several standard procedures and used solely when and if available.Fly T-WEoN is available as a Cytoscape application that permits integration with other tools, and facilitates downstream network analysis. In this work, we first demonstrate the reliability of our method to then provide a relevant application case of our tool: early development of D. melanogaster.AvailabilityFly T-WEoN, together with its step-by-step guide is available at https://[email protected]

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

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720016500451 ◽

2017 ◽

Vol 15 (02) ◽

pp. 1650045 ◽

Cited By ~ 1

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.

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Adaptive NetworkProfiler for Identifying Cancer Characteristic-Specific Gene Regulatory Networks

Journal of Computational Biology ◽

10.1089/cmb.2017.0120 ◽

2018 ◽

Vol 25 (2) ◽

pp. 130-145 ◽

Cited By ~ 3

Author(s):

Heewon Park ◽

Teppei Shimamura ◽

Seiya Imoto ◽

Satoru Miyano

Keyword(s):

Gene Regulatory Networks ◽

Regulatory Networks ◽

Specific Gene ◽

Gene Regulatory

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

10.1101/074716 ◽

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.

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

Cancer Informatics ◽

10.4137/cin.s19965 ◽

2015 ◽

Vol 14s4 ◽

pp. CIN.S19965 ◽

Cited By ~ 2

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.

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Inference of differential gene regulatory networks based on gene expression and genetic perturbation data

Bioinformatics ◽

10.1093/bioinformatics/btz529 ◽

2019 ◽

Vol 36 (1) ◽

pp. 197-204 ◽

Cited By ~ 2

Author(s):

Xin Zhou ◽

Xiaodong Cai

Keyword(s):

Gene Expression ◽

Gene Regulatory Networks ◽

Structural Equation ◽

Regulatory Networks ◽

Supplementary Information ◽

Specific Gene ◽

Joint Inference ◽

Perturbation Data ◽

Gene Regulatory ◽

The Difference

Abstract Motivation Gene regulatory networks (GRNs) of the same organism can be different under different conditions, although the overall network structure may be similar. Understanding the difference in GRNs under different conditions is important to understand condition-specific gene regulation. When gene expression and other relevant data under two different conditions are available, they can be used by an existing network inference algorithm to estimate two GRNs separately, and then to identify the difference between the two GRNs. However, such an approach does not exploit the similarity in two GRNs, and may sacrifice inference accuracy. Results In this paper, we model GRNs with the structural equation model (SEM) that can integrate gene expression and genetic perturbation data, and develop an algorithm named fused sparse SEM (FSSEM), to jointly infer GRNs under two conditions, and then to identify difference of the two GRNs. Computer simulations demonstrate that the FSSEM algorithm outperforms the approaches that estimate two GRNs separately. Analysis of a dataset of lung cancer and another dataset of gastric cancer with FSSEM inferred differential GRNs in cancer versus normal tissues, whose genes with largest network degrees have been reported to be implicated in tumorigenesis. The FSSEM algorithm provides a valuable tool for joint inference of two GRNs and identification of the differential GRN under two conditions. Availability and implementation The R package fssemR implementing the FSSEM algorithm is available at https://github.com/Ivis4ml/fssemR.git. It is also available on CRAN. Supplementary information Supplementary data are available at Bioinformatics online.

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Comparison of gene regulatory networks to identify pathogenic genes for lymphoma

Journal of Bioinformatics and Computational Biology ◽

10.1142/s0219720020500298 ◽

2020 ◽

Vol 18 (05) ◽

pp. 2050029

Author(s):

Xiao Yu ◽

Tongfeng Weng ◽

Changgui Gu ◽

Huijie Yang

Keyword(s):

Retinoic Acid ◽

Environmental Factors ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Retinoic Acid Receptor ◽

Specific Gene ◽

Acid Receptor ◽

Cancer Pathways ◽

Pathogenic Genes ◽

Gene Regulatory

Lymphoma is the most complicated cancer that can be divided into several tens of subtypes. It may occur in any part of body that has lymphocytes, and is closely correlated with diverse environmental factors such as the ionizing radiation, chemocarcinogenesis, and virus infection. All the environmental factors affect the lymphoma through genes. Identifying pathogenic genes for lymphoma is consequently an essential task to understand its complexity in a unified framework. In this paper, we propose a new method to expose high-confident edges in gene regulatory networks (GRNs) for a total of 32 organs, called Filtered GRNs (f-GRNs), comparison of which gives us a proper reference for the Lymphoma, i.e. the B-lymphocytes cells, whose f-GRN is closest with that for the Lymphoma. By using the Gene Ontology and Biological Process analysis we display the differences of the two networks’ hubs in biological functions. Matching with the Genecards shows that most of the hubs take part in the genetic information transmission and expression, except a specific gene of Retinoic Acid Receptor Alpha (RARA) that encodes the retinoic acid receptor. In the lymphoma, the genes in the RARA ego-network are involved in two cancer pathways, and the RARA is present only in these cancer pathways. For the lymphoid B cells, however, the genes in the RARA ego-network do not participate in cancer-related pathways.

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