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

2016 ◽  
Vol 113 (13) ◽  
pp. E1835-E1843 ◽  
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.

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]

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 Expresso: A database and web server for exploring the interaction of transcription factors and their target genes in Arabidopsis thaliana using ChIP-Seq peak data

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 372 ◽  
Author(s):  
Delasa Aghamirzaie ◽  
Karthik Raja Velmurugan ◽  
Shuchi Wu ◽  
Doaa Altarawy ◽  
Lenwood S. Heath ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Transcription Factors ◽  
Chromatin Immunoprecipitation ◽  
Target Genes ◽  
Regulation Of Gene Expression ◽  
Data Sets ◽  
Motif Analysis ◽  
Chromatin Immunoprecipitation Sequencing

Motivation: The increasing availability of chromatin immunoprecipitation sequencing (ChIP-Seq) data enables us to learn more about the action of transcription factors in the regulation of gene expression. Even though in vivo transcriptional regulation often involves the concerted action of more than one transcription factor, the format of each individual ChIP-Seq dataset usually represents the action of a single transcription factor. Therefore, a relational database in which available ChIP-Seq datasets are curated is essential. Results: We present Expresso (database and webserver) as a tool for the collection and integration of available Arabidopsis ChIP-Seq peak data, which in turn can be linked to a user’s gene expression data. Known target genes of transcription factors were identified by motif analysis of publicly available GEO ChIP-Seq data sets. Expresso currently provides three services: 1) Identification of target genes of a given transcription factor; 2) Identification of transcription factors that regulate a gene of interest; 3) Computation of correlation between the gene expression of transcription factors and their target genes. Availability: Expresso is freely available at http://bioinformatics.cs.vt.edu/expresso/

scholarly journals Nuclear Transfer Arrest Embryos Show Massive Dysregulation of Genes Involved in Transcription Pathways

2021 ◽  
Vol 22 (15) ◽  
pp. 8187
Author(s):  
Chunshen Long ◽  
Hanshuang Li ◽  
Xinru Li ◽  
Wuritu Yang ◽  
Yongchun Zuo
Keyword(s):  
Gene Regulatory Networks ◽  
Nuclear Transfer ◽  
Regulatory Networks ◽  
Target Genes ◽  
Epigenetic Modification ◽  
Cell Nuclei ◽  
Activation Of Transcription ◽  
Gene Regulatory ◽  
Catabolic Process

Somatic cell nuclear transfer (SCNT) technology can reprogram terminally differentiated cell nuclei into a totipotent state. However, the underlying molecular barriers of SCNT embryo development remain incompletely elucidated. Here, we observed that transcription-related pathways were incompletely activated in nuclear transfer arrest (NTA) embryos compared to normal SCNT embryos and in vivo fertilized (WT) embryos, which hinders the development of SCNT embryos. We further revealed the transcription pathway associated gene regulatory networks (GRNs) and found the aberrant transcription pathways can lead to the massive dysregulation of genes in NTA embryos. The predicted target genes of transcription pathways contain a series of crucial factors in WT embryos, which play an important role in catabolic process, pluripotency regulation, epigenetic modification and signal transduction. In NTA embryos, however, these genes were varying degrees of inhibition and show a defect in synergy. Overall, our research found that the incomplete activation of transcription pathways is another potential molecular barrier for SCNT embryos besides the incomplete reprogramming of epigenetic modifications, broadening the understanding of molecular mechanism of SCNT embryonic development.

Data-driven Gene Regulatory Networks Inference Based on Classification Algorithms

2021 ◽  
Vol 30 (04) ◽  
pp. 2150022
Author(s):  
Sergio Peignier ◽  
Pauline Schmitt ◽  
Federica Calevro
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Target Genes ◽  
Data Driven ◽  
Classification Algorithms ◽  
New Family ◽  
Gene Regulatory ◽  
High Throughput Gene Expression ◽  
Inference Methods

Inferring Gene Regulatory Networks from high-throughput gene expression data is a challenging problem, addressed by the systems biology community. Most approaches that aim at unraveling the gene regulation mechanisms in a data-driven way, analyze gene expression datasets to score potential regulatory links between transcription factors and target genes. So far, three major families of approaches have been proposed to score regulatory links. These methods rely respectively on correlation measures, mutual information metrics, and regression algorithms. In this paper we present a new family of data-driven inference methods. This new family, inspired by the regression-based paradigm, relies on the use of classification algorithms. This paper assesses and advocates for the use of this paradigm as a new promising approach to infer gene regulatory networks. Indeed, the development and assessment of five new inference methods based on well-known classification algorithms shows that the classification-based inference family exhibits good results when compared to well-established paradigms.

scholarly journals Randomization Strategies Affect Motif Significance Analysis in TF-miRNA-Gene Regulatory Networks

2017 ◽  
Vol 14 (2) ◽  
Author(s):  
Sepideh Sadegh ◽  
Maryam Nazarieh ◽  
Christian Spaniol ◽  
Volkhard Helms
Keyword(s):  
Transcription Factors ◽  
Gene Regulatory Networks ◽  
Degree Distribution ◽  
Regulatory Networks ◽  
Target Genes ◽  
Mirna Gene ◽  
Biological Cells ◽  
Significance Analysis ◽  
Gene Regulatory ◽  
Randomized Networks

AbstractGene-regulatory networks are an abstract way of capturing the regulatory connectivity between transcription factors, microRNAs, and target genes in biological cells. Here, we address the problem of identifying enriched co-regulatory three-node motifs that are found significantly more often in real network than in randomized networks. First, we compare two randomization strategies, that either only conserve the degree distribution of the nodes’ in- and out-links, or that also conserve the degree distributions of different regulatory edge types. Then, we address the issue how convergence of randomization can be measured. We show that after at most 10 × |E| edge swappings, converged motif counts are obtained and the memory of initial edge identities is lost.

scholarly journals Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Christopher A Jackson ◽  
Dayanne M Castro ◽  
Giuseppe-Antonio Saldi ◽  
Richard Bonneau ◽  
David Gresham
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Gene Regulatory Network ◽  
Gene Regulatory Networks ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Target Genes ◽  
Single Cell Rna Sequencing ◽  
Gene Regulatory

Understanding how gene expression programs are controlled requires identifying regulatory relationships between transcription factors and target genes. Gene regulatory networks are typically constructed from gene expression data acquired following genetic perturbation or environmental stimulus. Single-cell RNA sequencing (scRNAseq) captures the gene expression state of thousands of individual cells in a single experiment, offering advantages in combinatorial experimental design, large numbers of independent measurements, and accessing the interaction between the cell cycle and environmental responses that is hidden by population-level analysis of gene expression. To leverage these advantages, we developed a method for scRNAseq in budding yeast (Saccharomyces cerevisiae). We pooled diverse transcriptionally barcoded gene deletion mutants in 11 different environmental conditions and determined their expression state by sequencing 38,285 individual cells. We benchmarked a framework for learning gene regulatory networks from scRNAseq data that incorporates multitask learning and constructed a global gene regulatory network comprising 12,228 interactions.

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

2018 ◽  
Vol 83 (1) ◽  
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.

scholarly journals Gene regulatory networks controlled by FLOWERING LOCUS C that confer variation in seasonal flowering and life history

2020 ◽  
Author(s):  
Eva Madrid ◽  
John W Chandler ◽  
George Coupland
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Target Genes ◽  
Higher Plants ◽  
Floral Transition ◽  
Environmental Cues ◽  
Flowering Locus C ◽  
Gene Regulatory ◽  
Flowering Locus

Abstract Responses to environmental cues synchronize reproduction of higher plants to the changing seasons. The genetic basis of these responses has been intensively studied in the Brassicaceae. The MADS-domain transcription factor FLOWERING LOCUS C (FLC) plays a central role in the regulatory network that controls flowering of Arabidopsis thaliana in response to seasonal cues. FLC blocks flowering until its transcription is stably repressed by extended exposure to low temperatures in autumn or winter and, therefore, FLC activity is assumed to limit flowering to spring. Recent reviews describe the complex epigenetic mechanisms responsible for FLC repression in cold. We focus on the gene regulatory networks controlled by FLC and how they influence floral transition. Genome-wide approaches determined the in vivo target genes of FLC and identified those whose transcription changes during vernalization or in flc mutants. We describe how studying FLC targets such as FLOWERING LOCUS T, SQUAMOSA PROMOTER BINDING PROTEIN-LIKE 15, and TARGET OF FLC AND SVP 1 can explain different flowering behaviours in response to vernalization and other environmental cues, and help define mechanisms by which FLC represses gene transcription. Elucidating the gene regulatory networks controlled by FLC provides access to the developmental and physiological mechanisms that regulate floral transition.

scholarly journals Evolution of Mendelian dominance in gene regulatory networks associated with phenotypic robustness

2021 ◽  
Author(s):  
Kenji Okubo ◽  
Kunihiko Kaneko
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Target Genes ◽  
Gene Expression Pattern ◽  
Mendelian Inheritance ◽  
Gene Expression Dynamics ◽  
Phenotypic Robustness ◽  
Robustness To Noise ◽  
Gene Regulatory

AbstractMendelian inheritance is a fundamental law of genetics. Considering two alleles in a diploid, a phenotype of a heterotype is dominated by a particular homotype according to the law of dominance. This picture is usually based on simple genotype-phenotype mapping in which one gene regulates one phenotype. However, in reality, some interactions between genes can result in deviation from Mendelian dominance.Here, by using the numerical evolution of diploid gene regulatory networks (GRNs), we discuss whether Mendelian dominance evolves beyond the classical case of one-to-one genotype-phenotype mapping. We examine whether complex genotype-phenotype mapping can achieve Mendelian dominance through the evolution of the GRN with interacting genes. Specifically, we extend the GRN model to a diploid case, in which two GRN matrices are added to give gene expression dynamics, and simulate evolution with meiosis and recombination. Our results reveal that Mendelian dominance evolves even under complex genotype-phenotype mapping. This dominance is achieved via a group of genotypes that differ from each other but have a common phenotype given by the expression of target genes. Calculating the degree of dominance shows that it increases through the evolution, correlating closely with the decrease in phenotypic fluctuations and the increase in robustness to initial noise. This evolution of Mendelian dominance is associated with phenotypic robustness against meiosis-induced genome mixing, whereas sexual recombination arising from the mixing of chromosomes from the parents further enhances dominance and robustness. Owing to this dominance, the robustness to genetic differences increases, while the optimal fitness is sustained up to a large difference between the two genomes. In summary, Mendelian dominance is achieved by groups of genotypes that are associated with the increase in phenotypic robustness to noise.Author summaryMendelian dominance is one of the most fundamental laws in genetics. When two conflicting characters occur in a single diploid, the dominant character is always chosen. Assuming that one gene makes one character, this law is simple to grasp. However, in reality, phenotypes are generated via interactions between several genes, which may alter Mendel’s dominance law. The evolution of robustness to noise and mutations has been investigated extensively using complex expression dynamics with gene regulatory networks. Here, we applied gene-expression dynamics with complex interactions to the case of a diploid and simulated the evolution of the gene regulatory network to generate the optimal phenotype given by a certain gene expression pattern. Interestingly, after evolution, Mendelian dominance is achieved via a group of genes. This group-based Mendelian dominance is shaped by phenotype insensitivity to genome mixing by meiosis and evolves concurrently with the robustness to noise. By focusing on the influence of phenotypic robustness, which has received considerable attention recently, our result provides a novel perspective as to why Mendel’s law of dominance is commonly observed.

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