scholarly journals Expression pattern determines regulatory logic

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244864
Author(s):  
Carlos Mora-Martinez
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Dna Sequences ◽  
In Silico ◽  
Regulatory Networks ◽  
Expression Patterns ◽  
Cell Types ◽  
Evolutionary Strategy ◽  
Specific Gene ◽  
Gene Regulatory

Large amounts of effort have been invested in trying to understand how a single genome is able to specify the identity of hundreds of cell types. Inspired by some aspects of Caenorhabditis elegans biology, we implemented an in silico evolutionary strategy to produce gene regulatory networks (GRNs) that drive cell-specific gene expression patterns, mimicking the process of terminal cell differentiation. Dynamics of the gene regulatory networks are governed by a thermodynamic model of gene expression, which uses DNA sequences and transcription factor degenerate position weight matrixes as input. In a version of the model, we included chromatin accessibility. Experimentally, it has been determined that cell-specific and broadly expressed genes are regulated differently. In our in silico evolved GRNs, broadly expressed genes are regulated very redundantly and the architecture of their cis-regulatory modules is different, in accordance to what has been found in C. elegans and also in other systems. Finally, we found differences in topological positions in GRNs between these two classes of genes, which help to explain why broadly expressed genes are so resilient to mutations. Overall, our results offer an explanatory hypothesis on why broadly expressed genes are regulated so redundantly compared to cell-specific genes, which can be extrapolated to phenomena such as ChIP-seq HOT regions.

Regulation of gene expression in the nervous system

2008 ◽  
Vol 414 (3) ◽  
pp. 327-341 ◽  
Author(s):  
Lezanne Ooi ◽  
Ian C. Wood
Keyword(s):  
Gene Expression ◽  
Nervous System ◽  
Transcription Factors ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Molecular Mechanisms ◽  
Expression Patterns ◽  
Regulation Of Gene Expression ◽  
Cell Types ◽  
Gene Regulatory

The nervous system contains a multitude of cell types which are specified during development by cascades of transcription factors acting combinatorially. Some of these transcription factors are only active during development, whereas others continue to function in the mature nervous system to maintain appropriate gene-expression patterns in differentiated cells. Underpinning the function of the nervous system is its plasticity in response to external stimuli, and many transcription factors are involved in regulating gene expression in response to neuronal activity, allowing us to learn, remember and make complex decisions. Here we review some of the recent findings that have uncovered the molecular mechanisms that underpin the control of gene regulatory networks within the nervous system. We highlight some recent insights into the gene-regulatory circuits in the development and differentiation of cells within the nervous system and discuss some of the mechanisms by which synaptic transmission influences transcription-factor activity in the mature nervous system. Mutations in genes that are important in epigenetic regulation (by influencing DNA methylation and post-translational histone modifications) have long been associated with neuronal disorders in humans such as Rett syndrome, Huntington's disease and some forms of mental retardation, and recent work has focused on unravelling their mechanisms of action. Finally, the discovery of microRNAs has produced a paradigm shift in gene expression, and we provide some examples and discuss the contribution of microRNAs to maintaining dynamic gene regulatory networks in the brain.

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

2019 ◽  
Vol 36 (1) ◽  
pp. 197-204 ◽  
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.

scholarly journals Identification of context-specific gene regulatory networks with GEMULA—gene expression modeling using LAsso

2011 ◽  
Vol 28 (2) ◽  
pp. 214-221 ◽  
Author(s):  
Geert Geeven ◽  
Ronald E. van Kesteren ◽  
August B. Smit ◽  
Mathisca C. M. de Gunst
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Specific Gene ◽  
Gene Regulatory ◽  
Context Specific

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 Comparative analyses of saprotrophy in Salisapilia sapeloensis and diverse plant pathogenic oomycetes reveal lifestyle-specific gene expression

2020 ◽  
Vol 96 (11) ◽  
Author(s):  
Sophie de Vries ◽  
Jan de Vries ◽  
John M Archibald ◽  
Claudio H Slamovits
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Plant Pathogens ◽  
Molecular Mechanisms ◽  
Expression Patterns ◽  
Reference Points ◽  
Global Perspective ◽  
Specific Gene ◽  
Gene Regulatory ◽  
Specific Array

ABSTRACT Oomycetes include many devastating plant pathogens. Across oomycete diversity, plant-infecting lineages are interspersed by non-pathogenic ones. Unfortunately, our understanding of the evolution of lifestyle switches is hampered by a scarcity of data on the molecular biology of saprotrophic oomycetes, ecologically important primary colonizers of dead tissue that can serve as informative reference points for understanding the evolution of pathogens. Here, we established Salisapilia sapeloensis as a tractable system for the study of saprotrophic oomycetes. We generated multiple transcriptomes from S. sapeloensis and compared them with (i) 22 oomycete genomes and (ii) the transcriptomes of eight pathogenic oomycetes grown under 13 conditions. We obtained a global perspective on gene expression signatures of oomycete lifestyles. Our data reveal that oomycete saprotrophs and pathogens use similar molecular mechanisms for colonization but exhibit distinct expression patterns. We identify a S. sapeloensis-specific array and expression of carbohydrate-active enzymes and putative regulatory differences, highlighted by distinct expression levels of transcription factors. Salisapilia sapeloensis expresses only a small repertoire of candidates for virulence-associated genes. Our analyses suggest lifestyle-specific gene regulatory signatures and that, in addition to variation in gene content, shifts in gene regulatory networks underpin the evolution of oomycete lifestyles.

scholarly journals Precision of Tissue Patterning is Controlled by Dynamical Properties of Gene Regulatory Networks

2019 ◽  
Author(s):  
Katherine Exelby ◽  
Edgar Herrera-Delgado ◽  
Lorena Garcia Perez ◽  
Ruben Perez-Carrasco ◽  
Andreas Sagner ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Expression Patterns ◽  
Cell Fates ◽  
Gene Circuits ◽  
Stochastic Fluctuations ◽  
Network Properties ◽  
Gene Regulatory

AbstractDuring development, gene regulatory networks allocate cell fates by partitioning tissues into spatially organised domains of gene expression. How the sharp boundaries that delineate these gene expression patterns arise, despite the stochasticity associated with gene regulation, is poorly understood. We show, in the vertebrate neural tube, using perturbations of coding and regulatory regions, that the structure of the regulatory network contributes to boundary precision. This is achieved, not by reducing noise in individual genes, but by the configuration of the network modulating the ability of stochastic fluctuations to initiate gene expression changes. We use a computational screen to identify network properties that influence boundary precision, revealing two dynamical mechanisms by which small gene circuits attenuate the effect of noise in order to increase patterning precision. These results highlight design principles of gene regulatory networks that produce precise patterns of gene expression.

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

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]

scholarly journals Automated generation of context-specific gene regulatory networks with a weighted approach in Drosophila melanogaster

2021 ◽  
Vol 11 (4) ◽  
pp. 20200076 ◽  
Author(s):  
Leandro Murgas ◽  
Sebastian Contreras-Riquelme ◽  
J. Eduardo Martínez-Hernandez ◽  
Camilo Villaman ◽  
Rodrigo Santibáñez ◽  
...  
Keyword(s):  
Gene Expression ◽  
Drosophila Melanogaster ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Current Knowledge ◽  
Regulation Of Gene Expression ◽  
Reference Network ◽  
Specific Gene ◽  
Gene Regulatory ◽  
Context Specific

The regulation of gene expression is a key factor in the development and maintenance of life in all organisms. Even so, little is known at whole genome scale for most genes and contexts. We propose a method, Tool for Weighted Epigenomic Networks in Drosophila melanogaster (Fly T-WEoN), to generate context-specific gene regulatory networks starting from a reference network that contains all known gene regulations in the fly. Unlikely regulations 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 the 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 . Fly T-WEoN together with its step-by-step guide is available at https://weon.readthedocs.io .

scholarly journals Comparative analyses of saprotrophy in Salisapilia sapeloensis and diverse plant pathogenic oomycetes reveal lifestyle-specific gene expression

2019 ◽  
Author(s):  
Sophie de Vries ◽  
Jan de Vries ◽  
John M Archibald ◽  
Claudio H Slamovits
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Plant Pathogens ◽  
Molecular Mechanisms ◽  
Expression Patterns ◽  
Reference Points ◽  
Global Perspective ◽  
Specific Gene ◽  
Gene Regulatory ◽  
Specific Array

Oomycetes include many well-studied, devastating plant pathogens. Across oomycete diversity, plant-infecting lineages are interspersed by non-pathogenic ones. Unfortunately, our understanding of the evolution of lifestyle switches is hampered by a scarcity of data on the molecular biology of saprotrophic oomycetes, ecologically important primary colonizers of dead tissue that can serve as informative reference points for understanding the evolution of pathogens. Here, we established Salisapilia sapeloensis growing on axenic litter as a tractable system for the study of saprotrophic oomycetes. We generated multiple transcriptomes from S. sapeloensis and compared them to (a) 22 oomycete genomes and (b) the transcriptomes of eight pathogenic oomycetes grown under 13 conditions (three pathogenic lifestyles, six hosts/substrates, and four tissues). From these analyses we obtained a global perspective on the gene expression signatures of oomycete lifestyles. Our data reveal that oomycete saprotrophs and pathogens use generally similar molecular mechanisms for colonization, but exhibit distinct expression patterns. We identify S. sapeloensis' specific array and expression of carbohydrate-active enzymes and regulatory differences in pathogenicity-associated factors, including the virulence factor EpiC2B. Further, S. sapeloensis was found to express only a small repertoire of effector genes. In conclusion, our analyses reveal lifestyle-specific gene regulatory signatures and suggest that, in addition to variation in gene content, shifts in gene regulatory networks might underpin the evolution of oomycete lifestyles.

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