scholarly journals The gene regulatory system for specifying germ layers in early embryos of the simple chordate

2021 ◽  
Vol 7 (24) ◽  
pp. eabf8210
Author(s):  
Miki Tokuoka ◽  
Kazuki Maeda ◽  
Kenji Kobayashi ◽  
Atsushi Mochizuki ◽  
Yutaka Satou
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Regulatory Genes ◽  
Cell Stage ◽  
Germ Layers ◽  
Early Embryos ◽  
Gene Expression Dynamics ◽  
Gene Regulatory ◽  
Regulatory Functions

In animal embryos, gene regulatory networks control the dynamics of gene expression in cells and coordinate such dynamics among cells. In ascidian embryos, gene expression dynamics have been dissected at the single-cell resolution. Here, we revealed mathematical functions that represent the regulatory logics of all regulatory genes expressed at the 32-cell stage when the germ layers are largely specified. These functions collectively explain the entire mechanism by which gene expression dynamics are controlled coordinately in early embryos. We found that regulatory functions for genes expressed in each of the specific lineages contain a common core regulatory mechanism. Last, we showed that the expression of the regulatory genes became reproducible by calculation and controllable by experimental manipulations. Thus, these regulatory functions represent an architectural design for the germ layer specification of this chordate and provide a platform for simulations and experiments to understand the operating principles of gene regulatory networks.

Dynamics of Bacterial Gene Regulatory Networks

2018 ◽  
Vol 47 (1) ◽  
pp. 447-467 ◽  
Author(s):  
David L. Shis ◽  
Matthew R. Bennett, ◽  
Oleg A. Igoshin
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Network Architecture ◽  
Regulatory Networks ◽  
Single Cells ◽  
Cell Physiology ◽  
Bacterial Cells ◽  
Bacterial Gene ◽  
Gene Expression Dynamics ◽  
Gene Regulatory

The ability of bacterial cells to adjust their gene expression program in response to environmental perturbation is often critical for their survival. Recent experimental advances allowing us to quantitatively record gene expression dynamics in single cells and in populations coupled with mathematical modeling enable mechanistic understanding on how these responses are shaped by the underlying regulatory networks. Here, we review how the combination of local and global factors affect dynamical responses of gene regulatory networks. Our goal is to discuss the general principles that allow extrapolation from a few model bacteria to less understood microbes. We emphasize that, in addition to well-studied effects of network architecture, network dynamics are shaped by global pleiotropic effects and cell physiology.

scholarly journals Predicting synchronized gene coexpression patterns from fibration symmetries in gene regulatory networks in bacteria

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ian Leifer ◽  
Mishael Sánchez-Pérez ◽  
Cecilia Ishida ◽  
Hernán A. Makse
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Networks ◽  
Network Structure ◽  
Regulatory Networks ◽  
Conceptual Tool ◽  
E Coli ◽  
Gene Coexpression ◽  
Expression Of Genes ◽  
Gene Expression Dynamics ◽  
Gene Regulatory

Abstract Background Gene regulatory networks coordinate the expression of genes across physiological states and ensure a synchronized expression of genes in cellular subsystems, critical for the coherent functioning of cells. Here we address the question whether it is possible to predict gene synchronization from network structure alone. We have recently shown that synchronized gene expression can be predicted from symmetries in the gene regulatory networks described by the concept of symmetry fibrations. We showed that symmetry fibrations partition the genes into groups called fibers based on the symmetries of their ’input trees’, the set of paths in the network through which signals can reach a gene. In idealized dynamic gene expression models, all genes in a fiber are perfectly synchronized, while less idealized models—with gene input functions differencing between genes—predict symmetry breaking and desynchronization. Results To study the functional role of gene fibers and to test whether some of the fiber-induced coexpression remains in reality, we analyze gene fibrations for the gene regulatory networks of E. coli and B. subtilis and confront them with expression data. We find approximate gene coexpression patterns consistent with symmetry fibrations with idealized gene expression dynamics. This shows that network structure alone provides useful information about gene synchronization, and suggest that gene input functions within fibers may be further streamlined by evolutionary pressures to realize a coexpression of genes. Conclusions Thus, gene fibrations provide a sound conceptual tool to describe tunable coexpression induced by network topology and shaped by mechanistic details of gene expression.

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.

scholarly journals Systematic discovery and perturbation of regulatory genes in human T cells reveals the architecture of immune networks

2021 ◽  
Author(s):  
Jacob W Freimer ◽  
Oren Shaked ◽  
Sahin Naqvi ◽  
Nasa Sinnott-Armstrong ◽  
Arwa Kathiria ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Target Genes ◽  
Regulatory Genes ◽  
Disease Genes ◽  
Human T Cells ◽  
Gene Regulatory ◽  
Upstream Regulators

Complex gene regulatory networks ensure that important genes are expressed at precise levels. When gene expression is sufficiently perturbed it can lead to disease. To understand how gene expression disruptions percolate through a network, we must first map connections between regulatory genes and their downstream targets. However, we lack comprehensive knowledge of the upstream regulators of most genes. Here we developed an approach for systematic discovery of upstream regulators of critical immune factors - IL2RA, IL-2, and CTLA4 - in primary human T cells. Then, we mapped the network of these regulators' target genes and enhancers using CRISPR perturbations, RNA-Seq, and ATAC-Seq. These regulators form densely interconnected networks with extensive feedback loops. Furthermore, this network is highly enriched for immune-associated disease variants and genes. These results provide insight into how immune-associated disease genes are regulated in T cells and broader principles about the structure of human gene regulatory networks.

scholarly journals GATA-targeted compounds modulate cardiac subtype cell differentiation in dual reporter stem cell line

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Mika J. Välimäki ◽  
Robert S. Leigh ◽  
Sini M. Kinnunen ◽  
Alexander R. March ◽  
Ana Hernández de Sande ◽  
...  
Keyword(s):  
Gene Expression ◽  
Progenitor Cells ◽  
Cell Fate ◽  
Reporter Gene ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Cell Fate Decisions ◽  
Dual Reporter ◽  
Gene Regulatory ◽  
Reporter Gene Assays

AbstractBackgroundPharmacological modulation of cell fate decisions and developmental gene regulatory networks holds promise for the treatment of heart failure. Compounds that target tissue-specific transcription factors could overcome non-specific effects of small molecules and lead to the regeneration of heart muscle following myocardial infarction. Due to cellular heterogeneity in the heart, the activation of gene programs representing specific atrial and ventricular cardiomyocyte subtypes would be highly desirable. Chemical compounds that modulate atrial and ventricular cell fate could be used to improve subtype-specific differentiation of endogenous or exogenously delivered progenitor cells in order to promote cardiac regeneration.MethodsTranscription factor GATA4-targeted compounds that have previously shown in vivo efficacy in cardiac injury models were tested for stage-specific activation of atrial and ventricular reporter genes in differentiating pluripotent stem cells using a dual reporter assay. Chemically induced gene expression changes were characterized by qRT-PCR, global run-on sequencing (GRO-seq) and immunoblotting, and the network of cooperative proteins of GATA4 and NKX2-5 were further explored by the examination of the GATA4 and NKX2-5 interactome by BioID. Reporter gene assays were conducted to examine combinatorial effects of GATA-targeted compounds and bromodomain and extraterminal domain (BET) inhibition on chamber-specific gene expression.ResultsGATA4-targeted compounds 3i-1000 and 3i-1103 were identified as differential modulators of atrial and ventricular gene expression. More detailed structure-function analysis revealed a distinct subclass of GATA4/NKX2-5 inhibitory compounds with an acetyl lysine-like domain that contributed to ventricular cells (%Myl2-eGFP+). Additionally, BioID analysis indicated broad interaction between GATA4 and BET family of proteins, such as BRD4. This indicated the involvement of epigenetic modulators in the regulation of GATA-dependent transcription. In this line, reporter gene assays with combinatorial treatment of 3i-1000 and the BET bromodomain inhibitor (+)-JQ1 demonstrated the cooperative role of GATA4 and BRD4 in the modulation of chamber-specific cardiac gene expression.ConclusionsCollectively, these results indicate the potential for therapeutic alteration of cell fate decisions and pathological gene regulatory networks by GATA4-targeted compounds modulating chamber-specific transcriptional programs in multipotent cardiac progenitor cells and cardiomyocytes. The compound scaffolds described within this study could be used to develop regenerative strategies for myocardial regeneration.

scholarly journals Modeling transcriptional regulation using gene regulatory networks based on multi-omics data sources

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Neel Patel ◽  
William S. Bush
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Transcriptional Regulation ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Prediction Models ◽  
Long Distance ◽  
Chromatin Looping ◽  
Gene Regulatory ◽  
The Impact

Abstract Background Transcriptional regulation is complex, requiring multiple cis (local) and trans acting mechanisms working in concert to drive gene expression, with disruption of these processes linked to multiple diseases. Previous computational attempts to understand the influence of regulatory mechanisms on gene expression have used prediction models containing input features derived from cis regulatory factors. However, local chromatin looping and trans-acting mechanisms are known to also influence transcriptional regulation, and their inclusion may improve model accuracy and interpretation. In this study, we create a general model of transcription factor influence on gene expression by incorporating both cis and trans gene regulatory features. Results We describe a computational framework to model gene expression for GM12878 and K562 cell lines. This framework weights the impact of transcription factor-based regulatory data using multi-omics gene regulatory networks to account for both cis and trans acting mechanisms, and measures of the local chromatin context. These prediction models perform significantly better compared to models containing cis-regulatory features alone. Models that additionally integrate long distance chromatin interactions (or chromatin looping) between distal transcription factor binding regions and gene promoters also show improved accuracy. As a demonstration of their utility, effect estimates from these models were used to weight cis-regulatory rare variants for sequence kernel association test analyses of gene expression. Conclusions Our models generate refined effect estimates for the influence of individual transcription factors on gene expression, allowing characterization of their roles across the genome. This work also provides a framework for integrating multiple data types into a single model of transcriptional regulation.

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