Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Reconstruction of dynamic regulatory networks reveals signaling-induced topology changes associated with germ layer specification

ABSTRACT: Elucidating regulatory relationships between transcription factors (TFs) and target genes is fundamental to understanding how cells control their identity and behavior. To overcome the limited scope and scalability of experimental methods in mapping this control, computational GRN reconstruction methods have been developed to infer relationships from transcriptomic data. We developed a new computational GRN reconstruction tool called Epoch, which takes advantage of single-cell RNA sequencing to address a number of shortcomings of existing tools including low precision, low sensitivity, and the limited ability to describe network topology changes over time. We used Epoch to identify the dynamic networks and key transcriptional regulators underpinning directed differentiation of mouse embryonic stem cells, and further demonstrate that modulating signaling pathways drives topological network changes that shape cell fate potential. By integrating signaling pathways with GRN structures, we traced how Wnt activation and PI3K suppression govern mesoderm and endoderm specification, respectively.

ORGANISM(S): Mus musculus

PROVIDER: GSE177051 | GEO | 2022/01/27

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Functional Genomic Analyses Identify Pathways Dysregulated by Progranulin Deficiency Implicating Wnt Signaling

Project description:Progranulin (GRN) mutations cause frontotemporal dementia (FTD), but GRN's function in the CNS remains largely unknown. To identify the pathways downstream of GRN, we used weighted genome co-expression network analysis (WGCNA) to develop a systems-level view of transcriptional alterations in a human neural progenitor model of GRN-deficiency. This highlighted key pathways such as apoptosis and ubiquitination in GRN deficient human neurons, while revealing an unexpected major role for the Wnt signaling pathway, which was confirmed by analysis of gene expression data from postmortem FTD brain. Furthermore, we observed that the Wnt receptor Fzd2 was one of only a few genes up-regulated at 6 weeks in a GRN knockout mouse, and that FZD2 reduction caused increased apoptosis, while its upregulation promoted neuronal survival in vitro. Together, these in vitro and in vivo data point to an adaptive role for altered Wnt signaling in GRN deficiency-mediated FTD, representing a potential therapeutic target. We therefore developed an in vitro model of GRN deficiency using primary human neural stem cells in which shRNA was used to diminish GRN levels to 50% or below. We developed a tetracycline inducible system in which transactivator protein rtTA3 and PuroR genes were constituitively expressed under the UBC promoter, while RFP and shRNA were regulated by an inducible tet-On CMV promoter (Gossen and Bujard, 1992). To control for off-target effects, two hairpins against GRN were used, and a scrambled hairpin was used as a control.

2011-08-31 | E-GEOD-31772 | biostudies-arrayexpress

Empirical Bayes Conditional Independence Graphs for Dense Regulatory Network Recovery

Project description:Motivation: Computational inference methods that make use of graphical models to extract regulatory networks from gene expression data can have difficulty reconstructing dense regions of a network, a consequence of both computational complexity and unreliable parameter estimation when sample size is small. As a result, identification of hub genes is of special difficulty for these methods.Methods: We present a new algorithm, Empirical Light Mutual Min (ELMM), for large network reconstruction that has properties well suited for dense graph recovery. ELMM reconstructs the undirected graph of a regulatory network using empirical Bayes conditional independence testing with a heuristic relaxation of independence constraints in dense areas of the graph. This relaxation allows only one gene of a pair with a putative relation to be aware of the network connection, an approach that is aimed at easing multiple testing problems associated with recovering densely connected structures.Results: Using in silico data, we show that ELMM has better performance than commonly used network inference algorithms including PC Algorithm, GeneNet, and ARACNE. We also apply ELMM to reconstruct a network among 5,400 genes expressed in human lung airway epithelium of healthy nonsmokers, healthy smokers, and smokers with pulmonary diseases assayed using microarrays. The analysis identifies dense subnetworks that are consistent with known regulatory relationships in the lung airway and also suggests novel hub regulatory relationships among a number of genes that play roles in oxidative stress, wound response, and secretion.

2024-06-05 | GSE32030 | GEO

Modeling signaling-dependent pluripotent cell states with boolean logic can predict cell fate transitions [II]

Project description:Pluripotent stem cells (PSCs) exist in multiple stable states, each with specific cellular properties and molecular signatures. The process by which pluripotency is either maintained or destabilized to initiate specific developmental programs is poorly understood. We have developed a model to predict stabilized PSC gene regulatory network (GRN) states in response to combinations of input signals. While previous attempts to model PSC fate have been limited to static cell compositions, our approach enables simulations of dynamic heterogeneity by combining an Asynchronous Boolean Simulation (ABS) strategy with simulated single cell fate transitions using a Strongly Connected Components (SCCs). This computational framework was applied to a reverse-engineered and curated core GRN for mouse embryonic stem cells (mESCs) to simulate responses to LIF, Wnt/β-catenin, FGF/ERK, BMP4, and Activin A/Nodal pathway activation. For these input signals, our simulations exhibit strong predictive power for gene expression patterns, cell population composition, and nodes controlling cell fate transitions. The model predictions extend into early PSC differentiation, demonstrating, for example, that a Cdx2-high/Oct4-low state can be efficiently generated from mESCs residing in a naïve and signal-receptive state sustained by combinations of signaling activators and inhibitors.

2018-02-06 | GSE88927 | GEO

Modeling signaling-dependent pluripotent cell states with boolean logic can predict cell fate transitions [I]

2018-02-06 | GSE88926 | GEO

Expression data from poplar roots under nitrogen limitation

Project description:We study the effect of nitrogen limitation on the growth and development of poplar roots. We used microarrays to detail the global program of gene expression underlying morphological and developmental changes driven by low nitrogen in the growth media. We report the effect of nitrogen limitation on the growth and development of poplar roots. Low nitrogen concentration led to increased root elongation followed by lateral root proliferation and finally increased root biomass. These morphological responses correlated with high and specific activation of genes encoding regulators of cell cycle and enzymes involved in cell wall biogenesis, growth and remodeling. Comparative analysis of poplar and Arabidopsis root transcriptomes under nitrogen deficiency indicated many similarities and diversification in the response in the two species. A reconstruction of genetic regulatory network (GRN) analysis revealed a sub-network centered on a PtaNAC1-like transcription factor. Consistent with the GRN predictions, root-specific upregulation of PtaNAC1 in transgenic poplar plants increased root biomass and led to significant changes in the expression of the connected genes specifically under low nitrogen. PtaNAC1 and its regulatory miR164 showed inverse expression profiles during response to LN, suggesting of a micro RNA mediated attenuation of PtaNAC1 transcript abundance in response to nitrogen deprivation. Poplar roots from low nitrogen treated and untreated from in vitro condition was selected for RNA extraction and hybridization on Affymetrix microarrays. Roots were sampled at 6, 12, 24, 48, 96 and 504h after transfer to control and low nitrogen media and RNA was extacted.

2013-07-17 | E-GEOD-43162 | biostudies-arrayexpress

Mapping gene regulatory networks in Drosophila eye development by large-scale transcriptome perturbations and motif inference [ChIP-Seq]

Project description:Genome control is operated by transcription factors (TF) controlling their target genes by binding to promoters and enhancers. Conceptually, the interactions between TFs, their binding sites, and their functional targets are represented by gene regulatory networks (GRN). Deciphering in vivo GRNs underlying organ development in an unbiased genome-wide setting involves identifying both functional TF-gene interactions and physical TF-DNA interactions. To reverse-engineer the GRN of eye development in Drosophila, we performed RNA-seq across 72 genetic perturbations and sorted cell types, and inferred a co-expression network. Next, we derived direct TF-DNA interactions using computational motif inference, ultimately connecting 241 TFs to 5632 direct target genes through 24926 enhancers. Using this network we found network motifs, cis-regulatory codes, and new regulators of eye development. We validate the predicted target regions of Grainyhead by ChIP-seq and identify this factor as a general co-factor in the eye network, being bound to thousands of nucleosome-free regions.

2014-12-01 | GSE62557 | GEO

Mapping gene regulatory networks in Drosophila eye development by large-scale transcriptome perturbations and motif inference. [RNA-seq]

2014-12-01 | GSE59059 | GEO

Integrative single-cell chromatin and transcriptome profiling uncovers cell-type specific regulatory interactions

Project description:Simultaneous measurement of different molecular aspects of gene regulation can help infer functional relationships between genome regulation and gene expression, and understand their distinct and contribution to cell phenotype. Here, we present SHARE-seq, a highly scalable, sensitive, and cost-effective approach for joint measurement of chromatin accessibility and gene expression from the same single cells. Applying SHARE-seq to adult mouse tissues (skin, brain, lung) we show a direct congruence in cell type definition by either chromatin accessibility or RNA expression and create cell-type specific regulatory maps. We leverage naturally occurring cell-cell variation to infer chromatin-expression regulatory relationships and develop a broadly applicable computational strategy to define cis- ad trans- regulatory programs. The cis-regulatory programs, which largely overlap with super-enhancers, are found at key lineage-specifying genes. We demonstrate dynamic chromatin evidence of both gene expression lineage-priming and lineage-memory. The combined scalability and depth of SHARE-seq provide an extensible platform to study the regulatory relationships governing chromatin and gene expression changes across diverse cells within tissues.

2020-10-23 | GSE140203 | GEO

Gene Regulatory Network Reconfiguration in Direct Lineage Reprogramming

Project description:In direct lineage reprogramming, transcription factor (TF) overexpression reconfigures Gene Regulatory Networks (GRNs) to convert cell identities between fully differentiated cell types. We previously developed CellOracle, a computational pipeline that integrates single-cell transcriptome and epigenome profiles to infer GRNs. CellOracle leverages these inferred GRNs to simulate gene expression changes in response to TF perturbation, enabling network re-configuration during reprogramming to be interrogated in silico. Here, we integrate CellOracle analysis with lineage tracing of fibroblast to induced endoderm progenitor (iEP) conversion, a prototypical direct lineage reprogramming paradigm. By linking early network state to reprogramming success or failure, we reveal distinct network configurations underlying different reprogramming outcomes. Using these network analyses and in silico simulation of TF perturbation, we identify new factors to coax cells into successfully converting cell identity, uncovering a central role for the AP-1 subunit Fos with the Hippo signaling effector, Yap1. Together, these results demonstrate the efficacy of CellOracle to infer and interpret cell-type-specific GRN configurations at high resolution, providing new mechanistic insights into the regulation and reprogramming of cell identity.

2022-12-20 | GSE217675 | GEO

Project description:EPOCH Study

| PRJEB30346 | ENA