Transcriptional Landscape of Cardiomyocyte Maturation
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE73233 | GEO | 2015/11/16
SECONDARY ACCESSION(S): PRJNA296428
REPOSITORIES: GEO
Similar Datasets
Project description:Human hematopoiesis starts at early yolk sac and undergoes site- and stage-specific changes over development. However, the gene regulatory networks (GRN) that drive the developmental changes in naive hematopoietic stem/progenitor cells (HSPCs) remain poorly understood. Here, we analyzed single-cell transcriptome of human naive HSPCs in different developmental stages, including yolk-sac (YS), AGM, fetal liver (FL), umbilical cord blood (UCB) and adult peripheral blood (PB) mobilized HSPCs. These stage-specific HSPCs display differential intrinsic properties, such as metabolism, self-renewal, differentiating potentialities etc. We generated highly co-related GRN modules underlying the differential HSC key properties. Particularly, we identified GRNs and key regulators controlling lymphoid potentiality, self-renewal and aerobic respiration in human HSCs. Introducing selected regulators promotes establishing key HSC functions in HSPCs derived from human pluripotent stem cells. Therefore, GRNs underlying key intrinsic properties of human HSCs provide a valuable guide to generate fully functional HSCs in vitro.
2024-03-19 | GSE224972 | GEO
Project description:Deciphering gene regulatory networks (GRNs) is a key for understanding gene expression regulations in living systems. Here, we describe the investigation of the ABSCISIC ACID INSENSITIVE 3 (ABI3) plant transcription factor GRN vicinity by a technique called Network Walking. The method involves transient transformation of protoplasts and inducible nuclear re-localization of transcription factors along with transcriptomic analysis. This genome-wide approach allowed the de novo recovery of i) direct and indirect ABI3 target genes, ii) cis-binding site preference, and iii) biological processes regulated by this canonical abscisic acid response factor. This work improves our knowledge of ABI3 action by inferring network motifs (such as Feed Forwar Loops) under its influence. The novel high-throughput-oriented technique will help accelerate GRN systems investigations in plants, as well as in other organisms. This work studies ABI3 direct and indirect targets by a technique named Network Walking. Root/protoplasts were treated with or without dexamethasone (DEX) and cycloheximide (CHX). 3 reps each.
2013-01-01 | E-GEOD-33344 | biostudies-arrayexpress
Project description:Transcriptional control of any biological process occurs through the action of one to many gene regulatory networks (GRNs), but studies analyzing GRN interaction are lacking. To address this, we tested for the first time in vivo the hypothesis that two independent GRNs, which are active during formation of two discrete skeletal cell types (immature chondrocytes and osteoblasts), interact during differentiation of a third skeletal cell type (mature chondrocytes). These three cell types were isolated from the mouse embryo specifically using laser capture microdissection, and then RNA was extracted. Multiple analyses of corresponding RNA-seq data supported the hypothesis. Gene co-expression network analyses of differentially expressed genes suggested that one GRN containing the chondrogenic transcription factor Sox9 characterizes immature chondrocytes, one GRN containing the osteogenic transcription factor Runx2 characterizes osteoblasts, and both GRNs operate in mature chondrocytes. Indeed, mature chondrocytes differentially expressed fewer genes than the other cell types, consistent with the idea that overlapping actions of the immature chondrocyte and osteoblast GRNs regulate mature chondrocytes. Clustering analyses provided molecular insights into potential GRN interactions. Several genes in mature chondrocytes had expression levels that represented an averaging between levels in the immature chondrocyte and osteoblast. Interestingly, expression levels of one gene cluster, containing the hallmark mature chondrocyte genes Collagen type 10a1 and Indian hedgehog, suggested a synergistic interaction between immature chondrocyte and osteoblast GRNs. In addition to identifying novel genes expressed in mature chondrocytes, these results outline a novel in vivo experimental system through which to understand GRN organization and interaction.
2018-03-01 | GSE110051 | GEO
Project description:Gene regulatory networks (GRNs) provide a powerful framework for studying cellular differentiation. However, it is less clear how GRNs encode cellular responses to everyday microenvironmental cues. Macrophages can be polarized and potentially repolarized based on environmental signaling. In order to identify the GRNs that drive macrophage polarization and the heterogeneous single-cell subpopulations that are present in the process, we used a high-resolution time course of bulk and single-cell RNA-seq and ATAC-seq assays of HL-60-derived macrophages polarized towards M1 or M2 over 24 hours. We identified transient M1 and M2 markers, including the main transcription factors that underlie polarization, and subpopulations of naive, transitional, and terminally polarized macrophages. We built bulk and single-cell polarization GRNs to compare the recovered interactions and found that each technology recovered only a subset of known interactions. Our data provide a resource to study the GRN of cellular maturation in response to microenvironmental stimuli in a variety of contexts in homeostasis and disease.
2021-01-19 | GSE164498 | GEO
Project description:This SuperSeries is composed of the SubSeries listed below.This contains multi-omics datasets transcriptomic (RNA-Seq), methylomic (WGBS), and epigenomics (ATAC-seq) obtained during onset of sexual maturation in Atlantic salmon. We used gene regulatory networks (GRNs) to integrate results from these multi-omic analyses to identify key regulators of maturation.
2020-08-28 | GSE157003 | GEO
Project description:The Drosophila sex determination hierarchy consists of a splicing cascade with sex-specific transcription directing somatic sexual dimorphism. Our understanding of this pathway, and many others, is incomplete. Here we pioneer an approach to expand our knowledge of gene regulatory networks (GRNs) by leveraging natural genetic variation. This approach is generalizable to any natural population, including humans. Two studies from Drosophila female head tissue were used – the DSPR collection (alleles from 15 natural variants) and F1-hybrid collection (alleles from heterozygotes of 75 isogenic lines crossed to w1118) – in a structural equation model (SEM) analysis. We expanded the sex hierarchy GRN by adding novel links among genes in the pathway and by adding novel genes to the pathway. A link from fruitless (fru) to Sex-lethal (Sxl) was found in both populations, which is supported by the presence of fru binding sites in the Sxl locus. The splicing factors male-specific lethal 2 and Rm62 were correctly identified as downstream targets of Sxl. There were 754 additional candidate genes for an expanded sex hierarchy GRN. These candidates were enriched for genes with sex-biased splicing and many components of the spliceosome were placed in the GRN. As with other population-genetic analyses, the number of alleles limits the number of observable interactions. Network expansion was only clear in the F1-hybrid population, which has an average of twice as many alleles as the DSPR population. Independent studies of doublesex and transformer mutants support many novel connections, including evidence for a link between the sex hierarchy and metabolism, with the inclusion of Insulin-like receptor in the sex hierarchy GRN. RNA sequencing was performed on mRNA derived from adult male or female heads, for a total of 9 samples. These samples included females that produce the male isoform of dsx [w/+;DsxD/dsxm+r15 (XX)], and two dsx mutants: females [w/+; dsxm+r15/dsxd+r3 (XX)] and males [w;dsxm+r15/dsxd+r3 (XY)]. Two wild type genotypes (Berlin and Canton-S) were sequenced at the same time, but have previously been published as part of GSE50515. There were at least 3 replicates from biological samples.
2015-09-01 | E-GEOD-67400 | biostudies-arrayexpress
Project description:Convergent phenotypic evolution is often caused by recurrent changes at particular nodes in the underlying gene regulatory networks (GRNs). The genes at such evolutionary 'hotspots' are thought to maximally affect the phenotype with minimal pleiotropic consequences. This has led to the suggestion that if a GRN is understood in sufficient detail, the path of evolution may be predictable. The repeated evolutionary loss of larval trichomes among Drosophila species is caused by the loss of shavenbaby (svb) expression. svb is also required for development of leg trichomes, but the evolutionary gain of trichomes in the 'naked valley' on T2 femurs in Drosophila melanogaster is caused by the loss of microRNA-92a (miR-92a) expression rather than changes in svb. We used RNAseq of second (T2) pupal legs (20-28 h after puparium formation) to compare the expression of trichome GRN genes between strains with a large and a small naked valley. We also compared the function of components between the larval and leg trichome GRNs to investigate why the genetic basis of trichome pattern evolution differs in these developmental contexts. Moreover, we used ATAC-seq of second (T2) pupal legs (20-28 h after puparium formation) to identify the pupal leg enhancers of svb. We found key differences between the two networks in both the genes employed, and in the regulation and function of common genes. These differences in the GRNs reveal why mutations in svb are unlikely to contribute to leg trichome evolution and how instead miR-92a represents the key evolutionary switch in this context. Our work shows that variability in GRNs across different developmental contexts, as well as whether a morphological feature is lost versus gained, influence the nodes at which a GRN evolves to cause morphological change. Therefore, our findings have important implications for understanding the pathways and predictability of evolution.
2018-04-18 | GSE113240 | GEO
Project description:Genetic regulatory networks (GRNs) regulate the flow of genetic information from the genome to expressed messenger RNAs (mRNAs) and thus are critical to controlling the phenotypic characteristics of cells. Numerous methods exist for profiling mRNA transcript levels and identifying protein-DNA binding interactions at the genome-wide scale. These enable researchers to determine the structure and output of transcriptional regulatory networks, but uncovering the complete structure and regulatory logic of GRNs remains a challenge. The field of GRN inference aims to meet this challenge using computational modeling to derive the structure and logic of GRNs from experimental data and to encode this knowledge in Boolean networks, Bayesian networks, ordinary differential equation (ODE) models, or other modeling frameworks. However, most existing models do not incorporate dynamic transcriptional data since it has historically been less widely available in comparison to “static” transcriptional data. We report the development of an evolutionary algorithm-based ODE modeling approach that integrates kinetic transcription data and the theory of attractor dynamics analysis to infer GRN architecture and regulatory logic. Our method outperformed six leading GRN inference methods, all of which do not incorporate kinetic transcriptional data in predicting regulatory connections among TFs when applied to a small-scale engineered synthetic GRN in S. cerevisiae. Moreover, we have shown the potential of our method to predict unknown transcription profiles that would be produced upon genetic perturbation of the GRN governing a two-state phenotypic switch in C. albicans. We established an iterative refinement strategy to facilitate candidate selection for experimentation and the experimental results in turn provide validation or improvement for the model. In this way, our GRN inference approach can expedite the development of a sophisticated mathematical model that accurately describes the structure and dynamics of the in vivo GRN.
2023-06-23 | GSE217461 | GEO
Project description:Genetic regulatory networks (GRNs) regulate the flow of genetic information from the genome to expressed messenger RNAs (mRNAs) and thus are critical to controlling the phenotypic characteristics of cells. Numerous methods exist for profiling mRNA transcript levels and identifying protein-DNA binding interactions at the genome-wide scale. These enable researchers to determine the structure and output of transcriptional regulatory networks, but uncovering the complete structure and regulatory logic of GRNs remains a challenge. The field of GRN inference aims to meet this challenge using computational modeling to derive the structure and logic of GRNs from experimental data and to encode this knowledge in Boolean networks, Bayesian networks, ordinary differential equation (ODE) models, or other modeling frameworks. However, most existing models do not incorporate dynamic transcriptional data since it has historically been less widely available in comparison to “static” transcriptional data. We report the development of an evolutionary algorithm-based ODE modeling approach that integrates kinetic transcription data and the theory of attractor dynamics analysis to infer GRN architecture and regulatory logic. Our method outperformed six leading GRN inference methods, all of which do not incorporate kinetic transcriptional data in predicting regulatory connections among TFs when applied to a small-scale engineered synthetic GRN in S. cerevisiae. Moreover, we have shown the potential of our method to predict unknown transcription profiles that would be produced upon genetic perturbation of the GRN governing a two-state phenotypic switch in C. albicans. We established an iterative refinement strategy to facilitate candidate selection for experimentation and the experimental results in turn provide validation or improvement for the model. In this way, our GRN inference approach can expedite the development of a sophisticated mathematical model that accurately describes the structure and dynamics of the in vivo GRN.
2023-06-23 | GSE217383 | GEO
Project description:This a model from the article:
Monte Carlo analysis of an ODE Model of the Sea Urchin Endomesoderm Network.
Kühn C, Wierling C, Kühn A, Klipp E, Panopoulou G, Lehrach H, Poustka AJ.
BMC Syst Biol.2009 Aug 23;3:83.
19698179,
Abstract:
BACKGROUND: Gene Regulatory Networks (GRNs) control the differentiation, specification and function of cells at the genomic level. The levels of interactions within large GRNs are of enormous depth and complexity. Details about many GRNs are emerging, but in most cases it is unknown to what extent they control a given process, i.e. the grade of completeness is uncertain. This uncertainty stems from limited experimental data, which is the main bottleneck for creating detailed dynamical models of cellular processes. Parameter estimation for each node is often infeasible for very large GRNs. We propose a method, based on random parameter estimations through Monte-Carlo simulations to measure completeness grades of GRNs. RESULTS: We developed a heuristic to assess the completeness of large GRNs, using ODE simulations under different conditions and randomly sampled parameter sets to detect parameter-invariant effects of perturbations. To test this heuristic, we constructed the first ODE model of the whole sea urchin endomesoderm GRN, one of the best studied large GRNs. We find that nearly 48% of the parameter-invariant effects correspond with experimental data, which is 65% of the expected optimal agreement obtained from a submodel for which kinetic parameters were estimated and used for simulations. Randomized versions of the model reproduce only 23.5% of the experimental data. CONCLUSION: The method described in this paper enables an evaluation of network topologies of GRNs without requiring any parameter values. The benefit of this method is exemplified in the first mathematical analysis of the complete Endomesoderm Network Model. The predictions we provide deliver candidate nodes in the network that are likely to be erroneous or miss unknown connections, which may need additional experiments to improve the network topology. This mathematical model can serve as a scaffold for detailed and more realistic models. We propose that our method can be used to assess a completeness grade of any GRN. This could be especially useful for GRNs involved in human diseases, where often the amount of connectivity is unknown and/or many genes/interactions are missing.
The paper describes several models, Mi, i=1...n, where M0 correspond to the unperturbed model and all the others correspond to the perturbed model. This model is the unperturbed model. The model reproduces figure 5 of the reference publication. The figures were generated by running 1 simulation, whereas in the paper the plotted values are the means of 800 simulations using randomly samples parameter sets. Additional information from the Author: The parameter that were randomly samples are the transcription parameters c_Proteins... and k_Proteins. The parameter were sampled from a lognormal distribution with sigma = 1.5 and mu = 0.5
This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2010 The BioModels Team.For more information see the terms of use.To cite BioModels Database, please use Le Novère N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.
2024-09-02 | BIOMD0000000235 | BioModels