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

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

Interface Focus ◽

10.1098/rsfs.2020.0076 ◽

2021 ◽

Vol 11 (4) ◽

pp. 20200076 ◽

Cited By ~ 1

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 .

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Identifying genetic modulators of the connectivity between transcription factors and their transcriptional targets

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1517140113 ◽

2016 ◽

Vol 113 (13) ◽

pp. E1835-E1843 ◽

Cited By ~ 8

Author(s):

Mina Fazlollahi ◽

Ivor Muroff ◽

Eunjee Lee ◽

Helen C. Causton ◽

Harmen J. Bussemaker

Keyword(s):

Gene Expression ◽

Transcription Factors ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Target Genes ◽

Regulation Of Gene Expression ◽

Nonsynonymous Mutation ◽

Gene Regulatory ◽

Genetic Modulators

Regulation of gene expression by transcription factors (TFs) is highly dependent on genetic background and interactions with cofactors. Identifying specific context factors is a major challenge that requires new approaches. Here we show that exploiting natural variation is a potent strategy for probing functional interactions within gene regulatory networks. We developed an algorithm to identify genetic polymorphisms that modulate the regulatory connectivity between specific transcription factors and their target genes in vivo. As a proof of principle, we mapped connectivity quantitative trait loci (cQTLs) using parallel genotype and gene expression data for segregants from a cross between two strains of the yeast Saccharomyces cerevisiae. We identified a nonsynonymous mutation in the DIG2 gene as a cQTL for the transcription factor Ste12p and confirmed this prediction empirically. We also identified three polymorphisms in TAF13 as putative modulators of regulation by Gcn4p. Our method has potential for revealing how genetic differences among individuals influence gene regulatory networks in any organism for which gene expression and genotype data are available along with information on binding preferences for transcription factors.

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Regulation of gene expression in the nervous system

Biochemical Journal ◽

10.1042/bj20080963 ◽

2008 ◽

Vol 414 (3) ◽

pp. 327-341 ◽

Cited By ~ 42

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.

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

Bioinformatics ◽

10.1093/bioinformatics/btr641 ◽

2011 ◽

Vol 28 (2) ◽

pp. 214-221 ◽

Cited By ~ 26

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

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Predicting genotype-specific gene regulatory networks

10.1101/2021.01.18.427134 ◽

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).

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Upregulation and cell specificity of C4 genes are derived from ancestral C3 gene regulatory networks

10.1101/2020.07.03.186395 ◽

2020 ◽

Author(s):

Pallavi Singh ◽

Sean R. Stevenson ◽

Ivan Reyna-Llorens ◽

Gregory Reeves ◽

Tina B. Schreier ◽

...

Keyword(s):

Transcription Factors ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Cell Types ◽

Chromatin Accessibility ◽

Transcript Accumulation ◽

Specific Expression ◽

Cell Specificity ◽

Distinct Cell ◽

Gene Regulatory

ABSTRACTThe efficient C4 pathway is based on strong up-regulation of genes found in C3 plants, but also compartmentation of their expression into distinct cell-types such as the mesophyll and bundle sheath. Transcription factors associated with these phenomena have not been identified. To address this, we undertook genome-wide analysis of transcript accumulation, chromatin accessibility and transcription factor binding in C4Gynandropsis gynandra. From these data, two models relating to the molecular evolution of C4 photosynthesis are proposed. First, increased expression of C4 genes is associated with increased binding by MYB-related transcription factors. Second, mesophyll specific expression is associated with binding of homeodomain transcription factors. Overall, we conclude that during evolution of the complex C4 trait, C4 cycle genes gain cis-elements that operate in the C3 leaf such that they become integrated into existing gene regulatory networks associated with cell specificity and photosynthesis.

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Expression pattern determines regulatory logic

PLoS ONE ◽

10.1371/journal.pone.0244864 ◽

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.

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Gene regulatory networks controlling vertebrate retinal regeneration

Science ◽

10.1126/science.abb8598 ◽

2020 ◽

Vol 370 (6519) ◽

pp. eabb8598 ◽

Cited By ~ 3

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.

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MarR Family Transcription Factors fromBurkholderiaSpecies: Hidden Clues to Control of Virulence-Associated Genes

Microbiology and Molecular Biology Reviews ◽

10.1128/mmbr.00039-18 ◽

2018 ◽

Vol 83 (1) ◽

Cited By ~ 7

Author(s):

Ashish Gupta ◽

Anuja Pande ◽

Afsana Sabrin ◽

Sudarshan S. Thapa ◽

Brennan W. Gioe ◽

...

Keyword(s):

Transcription Factors ◽

Gene Regulatory Networks ◽

Regulatory Networks ◽

Environmental Changes ◽

Phenotypic Diversity ◽

Regulation Of Gene Expression ◽

Virulence Determinants ◽

Human Pathogens ◽

Plant Host ◽

Gene Regulatory

SUMMARYSpecies within the genusBurkholderiaexhibit remarkable phenotypic diversity. Genomic plasticity, including genome reduction and horizontal gene transfer, has been correlated with virulence traits in several species. However, the conservation of virulence genes in species otherwise considered to have limited potential for infection suggests that phenotypic diversity may not be explained solely on the basis of genetic diversity. Instead, differential organization and control of gene regulatory networks may underlie many phenotypic differences. In this review, we evaluate how regulation of gene expression by members of the multiple antibiotic resistance regulator (MarR) family of transcription factors may contribute to shaping the physiological diversity ofBurkholderiaspecies, with a focus on the clinically relevant human pathogens. AllBurkholderiaspecies encode a relatively large number of MarR proteins, a feature common to bacteria that must respond to environmental changes such as those associated with host invasion. However, evolution of gene regulatory networks has likely resulted in orthologous transcription factors controlling disparate sets of genes. Adaptation to, and survival in, diverse habitats, including a human or plant host, is key to the success ofBurkholderiaspecies as (opportunistic) pathogens, and recent reports suggest that control of virulence-associated genes by MarR proteins features prominently among the survival strategies employed by these species. We suggest that identification of MarR regulons will contribute significantly to clarification of virulence determinants and phenotypic diversity.

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