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682 results for “Transcriptional Networks”
Predicting placenta transcriptional regulatory interactions based on spatial gene expression data and convolutional neural network
<p><strong>Aims:</strong> The dysfunction of placenta development is correlated to the defects of pregnancy and fetal growth. The detailed molecular mechanism of placenta development is not identified in human due to the lack of material in vivo. Image-based reconstructions of GRN are still very underdeveloped.</p> <p><strong>Methods and Results:</strong> In this study, first-trimester chorionic villus and decidua tissues were collected. Next, we present a machine-learning system to infer gene interaction networks of the human placenta from immunofluorescence images of trophoblast specific transcription factors obtained by a high-resolution scanner.</p> <p><strong>Conclusions:</strong> The experimental results show that deep learning models reveal regulatory roles that have not yet been fully recognized. The spatial expression data reveal new regulatory relationships that traditional experiments have failed to recognize, and has allowed the development of gene regulation networks based on the spatial distribution of gene expression. We demonstrate the effectiveness of this approach in building networks using high-resolution images of the human placenta. Our analysis is of certain significance for further exploration of the development of the placenta and the occurrence of pregnancy-related diseases in the future. The datasets and analysis provide a useful source for the researchers in the field of the maternal-fetal interface and the establishment of pregnancy.</p>
Simulations of gene regulatory networks with transcriptional adaptation
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Transcriptional networks underlying a primary ovarian insufficiency disorder in alligators naturally exposed to EDCs: Transformed read counts and supplementary materials
<p>Interactions between the endocrine system and environmental contaminants are responsible for impairing reproductive development and function. Despite the taxonomic diversity of affected species and attendant complexity inherent to natural systems, the underlying signaling pathways and cellular consequences are mostly studied in lab models. To resolve the genetic and endocrine pathways that mediate affected ovarian function in organisms exposed to endocrine disrupting contaminants in their natural environments, we assessed broad-scale transcriptional and steroidogenic responses to exogenous gonadotropin stimulation in juvenile alligators (<em>Alligator missippiensis</em>) originating from a lake with well-documented pollution (Lake Apopka, FL) and a nearby reference site (Lake Woodruff, FL). We found that individuals from Lake Apopka are charachterized by hyperandrogenism and display hyper-sensitive transcriptional responses to gonadotropin stimulation when compared to individuals from Lake Woodruff. Site-specific transcriptomic divergence appears to be driven by wholly distinct subsets of transcriptional regulators, indicating alterations to fundamental genetic pathways governing ovarian function. Consistent with broad-scale transcriptional differences, ovaries of Lake Apopka alligators displayed impediments to folliculogenesis, with larger germinal beds and decreased numbers of late-stage follicles. After resolving the ovarian transcriptome into clusters of co-expressed genes, most site-associated modules were correlated to ovarian follicule phenotypes across individuals. However, expression of two site-specific clusters were independent of ovarian cellular architecture and are hypothesized to represent alterations to cell-autonomous transcriptional programs. Collectively, our findings provide high resolution mapping of transcriptional patterns to specific reproductive function and advance our mechanistic understanding regarding impaired reproductive health in an established model of environmental endocrine disruption.</p>
The transcription factor network of E. coli steers global responses to shifts in RNAP concentration
<p><span>The robustness and sensitivity of gene networks to environmental changes</span><span> is critical for cell survival. How gene networks produce specific, chronologically ordered responses to genome-wide perturbations, while robustly maintaining homeostasis, remains an open question. We analysed if short- and mid-term genome-wide responses to shifts in RNA polymerase (RNAP) concentration are influenced by the <em>known</em> topology and logic of the transcription factor network (TFN) of <em>Escherichia coli</em>. We found that, at the gene cohort level, the magnitude of the single-gene, mid-term transcriptional responses to changes in RNAP concentration can be explained by the absolute difference between the gene's numbers of activating and repressing input transcription factors (TFs)</span><span>. Interestingly, this difference is strongly positively correlated with the number of input TFs of the gene. Meanwhile, short-term responses showed only weak influence from the TFN. </span><span>Our results suggest that the global topological traits of the TFN of <em>E. coli</em> shape which gene cohorts respond to genome-wide stresses.</span></p>
Transcriptional networks underlying a primary ovarian insufficiency disorder in alligators naturally exposed to EDCs: Transformed read counts and supplementary materials
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Tissue-type specific accumulation of the plastoglobular proteome, transcriptional networks and plastoglobular functions
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Supplementary data from: Bimodal retrograde signaling disrupts a suppressor network and activates a key transcriptional activator to direct stress responses
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The transcription factor network of E. coli steers global responses to shifts in RNAP concentration
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Analysis and Figures from "Causal network inference from gene transcriptional time-series response to glucocorticoids"
<p>Gene regulatory network inference is essential to uncover complex relationships among gene pathways and inform downstream experiments, ultimately enabling regulatory network re-engineering. Network inference from transcriptional time-series data requires accurate, interpretable, and efficient determination of causal relationships among thousands of genes. Here, we develop Bootstrap Elastic net regression from Time Series (BETS), a statistical framework based on Granger causality for the recovery of a directed gene network from transcriptional time-series data. BETS uses elastic net regression and stability selection from bootstrapped samples to infer causal relationships among genes. BETS is highly parallelized, enabling efficient analysis of large transcriptional data sets. We show competitive accuracy on a community benchmark, the DREAM4 100-gene network inference challenge, where BETS is one of the fastest among methods of similar performance and additionally infers whether the causal effects are activating or inhibitory. We apply BETS to transcriptional time-series data of 2,768 differentially-expressed genes from A549 cells exposed to glucocorticoids over a period of 12 hours. We identify a network of 2,768 genes and 31,945 directed edges (FDR <= 0.2). We validate inferred causal network edges using two external data sources: overexpression experiments on the same glucocorticoid system, and genetic variants associated with inferred edges in primary lung tissue in the Genotype-Tissue Expression (GTEx) v6 project. BETS is available as an open source software package at https://github.com/lujonathanh/BETS</p> <p>This upload documents the analysis and figure files that support each numerical claim of the manuscript. Full Progeny.xlsx lists out the relevant code and files for each numerical claim of the manuscript, assuming the home folder of port-from-della</p>
HAND2 Targets Define a Network of Transcriptional Regulators that Compartmentalize the Early Limb Bud Mesenchyme
<p>Highlights</p> <p>•</p> <p>ChIP-seq identifies the CRMs bound by endogenous HAND2 in embryos and limb buds</p> <p>•</p> <p>HAND2 controls key transcriptional regulators acting upstream of SHH in limb buds</p> <p>•</p> <p>These transcriptional circuits define proximal, anterior, and posterior identities</p> <p>•</p> <p>HAND2 establishes anterior and posterior compartments by regulating <em>Gli3</em> and <em>Tbx3</em></p>
Systematic dissection of transcriptional regulatory networks by genome-scale and single-cell CRISPR screens
Millions of putative transcriptional regulatory elements (TREs) have been cataloged in the human genome, yet their functional relevance in specific pathophysiological settings remains to be determined. This is critical to understand how oncogenic transcription factors (TFs) engage specific TREs to impose transcriptional programs underlying malignant phenotypes. Here, we combine cutting edge CRISPR screens and epigenomic profiling to functionally survey ≈15,000 TREs engaged by estrogen receptor (ER). We show that ER exerts its oncogenic role in breast cancer by engaging TREs enriched in GATA3, TFAP2C, and H3K27Ac signal. These TREs control critical downstream TFs, among which TFAP2C plays an essential role in ER-driven cell proliferation. Together, our work reveals novel insights into a critical oncogenic transcription program and provides a framework to map regulatory networks, enabling to dissect the function of the noncoding genome of cancer cells.
Data from: Temporal transcriptional logic of dynamic regulatory networks underlying nitrogen signaling and use in plants
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Systematic dissection of transcriptional regulatory networks by genome-scale and single-cell CRISPR screens
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Data from: Dynamic antagonism between phytochromes and PIF-family bHLHs induces selective reciprocal responses to light and shade in a rapidly responsive transcriptional network in Arabidopsis
Plants respond to shade-modulated light-signals, via the phytochrome (phy) system, by adaptive changes, collectively termed the shade avoidance syndrome (SAS). To examine the roles of the Phy-Interacting bHLH Factors, PIF1, 3, 4 and 5, in relaying this information to the transcriptional network, we compared the genome-wide expression profiles of wild-type and quadruple pif (pifq) mutants in response to shade. The data identify a subset of genes, enriched in transcription-factor-encoding loci, that respond rapidly (within 1 h), in a PIF-dependent manner, to the shade signal, and that contain promoter-located G-box-sequence motifs (CACGTG), known to be preferred PIF binding sites. These genes are thus potential direct targets of phy-PIF signaling that function in the primary transcriptional circuitry controlling downstream response-elaboration. A second subset of PIF-dependent, early-response genes, lacking G-box motifs, are enriched for auxin-responsive loci, suggestive of being indirect targets of phy-PIF signaling involved in the rapid cell-expansion known to be induced by shade. A meta-analysis comparing deetiolation- and shade-responsive transcriptomes identifies a further subset of G-box-containing genes that reciprocally display rapid repression and induction in response to light and shade signals at the inception of deetiolation and shade-avoidance, respectively. Collectively, these data define a core set of transcriptional and hormonal (auxin, cytokinin) processes that appear to be dynamically poised to react rapidly to changes in the light environment via perturbations in the mutually antagonistic actions of the phys and PIFs. Data from comparative analysis of the quadruple pifq and all triple pif-mutant combinations in response to light and shade, confirm that the PIF-quartet members act with overlapping redundancy on seedling morphogenesis and transcriptional regulation, but that the individual PIFs contribute differentially to these responses.
Rice genome-scale network integration reveals transcriptional regulators of grass cell wall synthesis
<p><span><span><span><span><span><span><span><span><span><span><span>Grasses have evolved distinct cell wall composition and patterning relative to dicotyledonous plants. However, despite the importance of this plant family, transcriptional regulation of its cell wall biosynthesis is poorly understood. To identify grass cell wall-associated transcription factors, we constructed the Rice Combined mutual Ranked Network (RCRN). The RCRN covers >90% of annotated rice (<i>Oryza sativa</i>) genes, is high quality, and includes most grass-specific cell wall genes, such as mixed-linkage glucan synthases and hydroxycinnamoyl acyltransferases. Comparing the RCRN and an equivalent <i>Arabidopsis </i>network suggests that grass orthologs of most genetically verified eudicot cell wall regulators also control this process in grasses, but some vary significantly in network connectivity between these divergent species. Reverse genetics, yeast-one-hybrid, and protoplast-based assays reveal that OsMYB61a activates a grass-specific acyltransferase promoter, which confirms network predictions and supports grass-specific cell wall synthesis genes being incorporated into conserved regulatory circuits. In addition, 10 of 15 tested transcription factors, including six novel <u>w</u>all-<u>a</u>ssociated regulators (WAP1, WACH1, WAHL1, WADH1, OsMYB13a, and OsMYB13b), alter abundance of cell wall-related transcripts when transiently expressed. The results highlight the quality of the RCRN for examining rice biology, provide insight into the evolution of cell wall regulation, and identify network nodes and edges that are possible leads for improving cell wall composition.</span></span></span></span></span></span></span></span></span></span></span></p>
Data from: Evolution of transcription networks in response to temporal fluctuations
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Data from: Dynamic antagonism between phytochromes and PIF-family bHLHs induces selective reciprocal responses to light and shade in a rapidly responsive transcriptional network in Arabidopsis
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Rice genome-scale network integration reveals transcriptional regulators of grass cell wall synthesis
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Generation of transcriptional regulatory network of Lgr5+ small and large intestinal stem cells from mouse [scRNA-seq]
GEO Series GSE196915. Mus musculus. 1 samples. Type: Expression profiling by high throughput sequencing.
Transcription factor networks disproportionately enrich for heritability of blood cell phenotypes [10x ATAC + GEX Multiome]
GEO Series GSE274113. Homo sapiens. 42 samples. Type: Other.
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