scholarly journals TAF4b transcription networks regulating early oocyte differentiation

2021 ◽  
Author(s):  
Megan A Gura ◽  
Sona Relovska ◽  
Kimberly M Abt ◽  
Kimberly A Seymour ◽  
Tong Wu ◽  
...  
Keyword(s):  
Ovarian Reserve ◽  
Regulatory Network ◽  
Target Genes ◽  
Tata Box ◽  
Regulatory Pathways ◽  
Gene Regulatory ◽  
Oocyte Differentiation ◽  
Transcription Networks ◽  
Inappropriate Expression ◽  
Promoter Interaction

Establishment of a healthy ovarian reserve is contingent upon numerous regulatory pathways during embryogenesis. Previously, mice lacking TBP-associated factor 4b (Taf4b) were shown to exhibit a diminished ovarian reserve. However, potential oocyte-intrinsic functions of TAF4b have not been examined. Here we use a combination of gene expression profiling and chromatin mapping to characterize the TAF4b gene regulatory network in mouse oocytes. We find that Taf4b-deficient oocytes display inappropriate expression of meiotic, chromatin, and X-linked genes, and unexpectedly we found a connection with Turner Syndrome pathways. Using Cleavage Under Targets and Release Using Nuclease (CUT&RUN), we observed TAF4b enrichment at genes involved in meiosis and DNA repair, some of which are differentially expressed in Taf4b-deficient oocytes. Interestingly, TAF4b target genes were enriched for Sp/KLF family motifs rather than TATA-box, suggesting an alternate mode of promoter interaction. Together, our data connects several gene regulatory nodes that contribute to the ovarian reserve.

scholarly journals Deciphering the genetic links between NAFLD and co-occurring conditions using a liver gene regulatory network

2021 ◽  
Author(s):  
Sreemol Gokuladhas ◽  
William Schierding ◽  
Roan Eltigani Zaied ◽  
Tayaza Fadason ◽  
Murim Choi ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Complex Traits ◽  
Target Genes ◽  
Specific Gene ◽  
Fat Percentage ◽  
Alcoholic Fatty Liver ◽  
Regulatory Interactions ◽  
Hepatic Diseases ◽  
Gene Regulatory

Background & Aims: Non-alcoholic fatty liver disease (NAFLD) is a multi-system metabolic disease that co-occurs with various hepatic and extra-hepatic diseases. The phenotypic manifestation of NAFLD is primarily observed in the liver. Therefore, identifying liver-specific gene regulatory interactions between variants associated with NAFLD and multimorbid conditions may help to improve our understanding of underlying shared aetiology. Methods: Here, we constructed a liver-specific gene regulatory network (LGRN) consisting of genome-wide spatially constrained expression quantitative trait loci (eQTLs) and their target genes. The LGRN was used to identify regulatory interactions involving NAFLD-associated genetic modifiers and their inter-relationships to other complex traits. Results and Conclusions: We demonstrate that MBOAT7 and IL32, which are associated with NAFLD progression, are regulated by spatially constrained eQTLs that are enriched for an association with liver enzyme levels. MBOAT7 transcript levels are also linked to eQTLs associated with cirrhosis, and other traits that commonly co-occur with NAFLD. In addition, genes that encode interacting partners of NAFLD-candidate genes within the liver-specific protein-protein interaction network were affected by eQTLs enriched for phenotypes relevant to NAFLD (e.g. IgG glycosylation patterns, OSA). Furthermore, we identified distinct gene regulatory networks formed by the NAFLD-associated eQTLs in normal versus diseased liver, consistent with the context-specificity of the eQTLs effects. Interestingly, genes targeted by NAFLD-associated eQTLs within the LGRN were also affected by eQTLs associated with NAFLD-related traits (e.g. obesity and body fat percentage). Overall, the genetic links identified between these traits expand our understanding of shared regulatory mechanisms underlying NAFLD multimorbidities.

scholarly journals Identification of the Q Gene Playing a Role in Spike Morphology Variation in Wheat Mutants and Its Regulatory Network

2022 ◽  
Vol 12 ◽  
Author(s):  
Jiazi Zhang ◽  
Hongchun Xiong ◽  
Huijun Guo ◽  
Yuting Li ◽  
Xiaomei Xie ◽  
...  
Keyword(s):  
Regulatory Network ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Expression Patterns ◽  
Spike Morphology ◽  
Regulatory Pathways ◽  
Dynamic Expression ◽  
Spike Development ◽  
Family Gene ◽  
Q Gene

The wheat AP2 family gene Q controls domestication traits, including spike morphology and threshability, which are critical for the widespread cultivation and yield improvement of wheat. Although many studies have investigated the molecular mechanisms of the Q gene, its direct target genes, especially those controlling spike morphology, are not clear, and its regulatory pathways are not well established. In this study, we conducted gene mapping of a wheat speltoid spike mutant and found that a new allele of the Q gene with protein truncation played a role in spike morphology variation in the mutant. Dynamic expression levels of the Q gene throughout the spike development process suggested that the transcript abundances of the mutant were decreased at the W6 and W7 scales compared to those of the WT. We identified several mutation sites on the Q gene and showed that mutations in different domains resulted in distinct phenotypes. In addition, we found that the Q gene produced three transcripts via alternative splicing and that they exhibited differential expression patterns in nodes, internodes, flag leaves, and spikes. Finally, we identified several target genes directly downstream of Q, including TaGRF1-2D and TaMGD-6B, and proposed a possible regulatory network. This study uncovered the target genes of Q, and the results can help to clarify the mechanism of wheat spike morphology and thereby improve wheat grain yield.

Prenet: Predictive network from ATAC-SEQ data

2020 ◽  
Vol 18 (01) ◽  
pp. 2040003 ◽  
Author(s):  
Nazmus Salehin ◽  
Patrick P. L. Tam ◽  
Pierre Osteil
Keyword(s):  
Regulatory Network ◽  
State Of The Art ◽  
Cell Types ◽  
Cell Type ◽  
Regulatory Pathways ◽  
Novel Approach ◽  
Multiple Cell ◽  
Bona Fide ◽  
Gene Regulatory ◽  
Accessible Chromatin

Assays for transposase-accessible chromatin sequencing (ATAC-seq) provides an innovative approach to study chromatin status in multiple cell types. Moreover, it is also possible to efficiently extract differentially accessible chromatin (DACs) regions by using state-of-the-art algorithms (e.g. DESeq2) to predict gene activity in specific samples. Furthermore, it has recently been shown that small dips in sequencing peaks can be attributed to the binding of transcription factors. These dips, also known as footprints, can be used to identify trans-regulating interactions leading to gene expression. Current protocols used to identify footprints (e.g. pyDNAse and HINT-ATAC) have shown limitations resulting in the discovery of many false positive footprints. We generated a novel approach to identify genuine footprints within any given ATAC-seq dataset. Herein, we developed a new pipeline embedding DACs together with bona fide footprints resulting in the generation of a Predictive gene regulatory Network (PreNet) simply from ATAC-seq data. We further demonstrated that PreNet can be used to unveil meaningful molecular regulatory pathways in a given cell type.

scholarly journals Identification of key tissue-specific, biological processes by integrating enhancer information in maize gene regulatory networks

2020 ◽  
Author(s):  
Maud Fagny ◽  
Marieke Lydia Kuijjer ◽  
Maike Stam ◽  
Johann Joets ◽  
Olivier Turc ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Binding Sites ◽  
Regulatory Network ◽  
Tissue Specificity ◽  
Target Genes ◽  
Specific Gene ◽  
Tissue Specific ◽  
Genome Wide ◽  
Gene Regulatory

AbstractEnhancers are important regulators of gene expression during numerous crucial processes including tissue differentiation across development. In plants, their recent molecular characterization revealed their capacity to activate the expression of several target genes through the binding of transcription factors. Nevertheless, identifying these target genes at a genome-wide level remains a challenge, in particular in species with large genomes, where enhancers and target genes can be hundreds of kilobases away. Therefore, the contribution of enhancers to regulatory network is still poorly understood in plants. In this study, we investigate the enhancer-driven regulatory network of two maize tissues at different stages: leaves at seedling stage and husks (bracts) at flowering. Using a systems biology approach, we integrate genomic, epigenomic and transcriptomic data to model the regulatory relationship between transcription factors and their potential target genes. We identify regulatory modules specific to husk and V2-IST, and show that they are involved in distinct functions related to the biology of each tissue. We evidence enhancers exhibiting binding sites for two distinct transcription factor families (DOF and AP2/ERF) that drive the tissue-specificity of gene expression in seedling immature leaf and husk. Analysis of the corresponding enhancer sequences reveals that two different transposable element families (TIR transposon Mutator and MITE Pif/Harbinger) have shaped the regulatory network in each tissue, and that MITEs have provided new transcription factor binding sites that are involved in husk tissue-specificity.SignificanceEnhancers play a major role in regulating tissue-specific gene expression in higher eukaryotes, including angiosperms. While molecular characterization of enhancers has improved over the past years, identifying their target genes at the genome-wide scale remains challenging. Here, we integrate genomic, epigenomic and transcriptomic data to decipher the tissue-specific gene regulatory network controlled by enhancers at two different stages of maize leaf development. Using a systems biology approach, we identify transcription factor families regulating gene tissue-specific expression in husk and seedling leaves, and characterize the enhancers likely to be involved. We show that a large part of maize enhancers is derived from transposable elements, which can provide novel transcription factor binding sites crucial to the regulation of tissue-specific biological functions.

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 Dissecting the DNA binding landscape and gene regulatory network of p63 and p53

2020 ◽  
Author(s):  
Konstantin Riege ◽  
Helene Kretzmer ◽  
Arne Sahm ◽  
Simon S. McDade ◽  
Steve Hoffmann ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Target Genes ◽  
De Novo ◽  
Therapeutic Interventions ◽  
Expression Studies ◽  
Gene Regulatory ◽  
Recognition Motifs ◽  
Gene Expression Studies ◽  
Search Approach

AbstractThe transcription factor (TF) p53 is the best-known tumor suppressor, but its ancient sibling p63 (ΔNp63) is a master regulator of epidermis development and a key oncogenic driver in squamous cell carcinomas (SCC). Despite multiple gene expression studies becoming available in recent years, the limited overlap of reported p63-dependent genes has made it difficult to decipher the p63 gene regulatory network (GRN). In particular, analyses of p63 response elements differed substantially among the studies. To address this intricate data situation, we provide an integrated resource that enables assessing the p63-dependent regulation of any human gene of interest. Here, we use a novel iterative de novo motif search approach in conjunction with extensive publicly available ChIP-seq data to achieve a precise global distinction between p53 and p63 binding sites, recognition motifs, and potential co-factors. We integrate all these data with enhancer:gene associations to predict p63 target genes and identify those that are commonly de-regulated in SCC and, thus, may represent candidates for therapeutic interventions.

The TATA-box motif and its impact on transcriptional gene regulation by miRNAs

2015 ◽  
Vol 6 (2) ◽  
pp. 157-161 ◽  
Author(s):  
Javier T. Granados-Riveron ◽  
Guillermo Aquino-Jarquin
Keyword(s):  
Gene Transcription ◽  
Transcriptional Control ◽  
Tata Box ◽  
Cellular Mirnas ◽  
Regulatory Pathways ◽  
Synergistic Approach ◽  
Nuclear Events ◽  
Transcriptional Modulation ◽  
Gene Regulatory ◽  
Transcriptional Gene Regulation

AbstractEmerging evidence suggests that components of the small RNAs pathway interact with chromatin to regulate nuclear events, such as gene transcription. However, it has recently been reported that in some cases, gene transcription regulation by cellular miRNAs can occur via targeting the TATA-box motif without altering epigenetic modifications. This observation supports the notion that multiple mechanisms of miRNA-based transcriptional regulation exist, enhancing our understanding of the complexity of small RNA-mediated gene regulatory pathways. Here, we remark that miRNA-mediated transcriptional modulation, through the TATA-box motif, may be a synergistic approach for transcriptional control.

scholarly journals SCENIC: Single-cell regulatory network inference and clustering

2017 ◽  
Author(s):  
Sara Aibar ◽  
Carmen Bravo González-Blas ◽  
Thomas Moerman ◽  
Jasper Wouters ◽  
Vân Anh Huynh-Thu ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Single Cell ◽  
Regulatory Network ◽  
Network Inference ◽  
Target Genes ◽  
Single Cells ◽  
State Transitions ◽  
Rna Seq ◽  
Cell State ◽  
Gene Regulatory

AbstractSingle-cell RNA-seq allows building cell atlases of any given tissue and infer the dynamics of cellular state transitions during developmental or disease trajectories. Both the maintenance and transitions of cell states are encoded by regulatory programs in the genome sequence. However, this regulatory code has not yet been exploited to guide the identification of cellular states from single-cell RNA-seq data. Here we describe a computational resource, called SCENIC (Single Cell rEgulatory Network Inference and Clustering), for the simultaneous reconstruction of gene regulatory networks (GRNs) and the identification of stable cell states, using single-cell RNA-seq data. SCENIC outperforms existing approaches at the level of cell clustering and transcription factor identification. Importantly, we show that cell state identification based on GRNs is robust towards batch-effects and technical-biases. We applied SCENIC to a compendium of single-cell data from the mouse and human brain and demonstrate that the proper combinations of transcription factors, target genes, enhancers, and cell types can be identified. Moreover, we used SCENIC to map the cell state landscape in melanoma and identified a gene regulatory network underlying a proliferative melanoma state driven by MITF and STAT and a contrasting network controlling an invasive state governed by NFATC2 and NFIB. We further validated these predictions by showing that two transcription factors are predominantly expressed in early metastatic sentinel lymph nodes. In summary, SCENIC is the first method to analyze scRNA-seq data using a network-centric, rather than cell-centric approach. SCENIC is generic, easy to use, and flexible, and allows for the simultaneous tracing of genomic regulatory programs and the mapping of cellular identities emerging from these programs. Availability: SCENIC is available as an R workflow based on three new R/Bioconductor packages: GENIE3, RcisTarget and AUCell. As scalable alternative to GENIE3, we also provide GRNboost, paving the way towards the network analysis across millions of single cells.

scholarly journals Dissecting the DNA binding landscape and gene regulatory network of p63 and p53

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Konstantin Riege ◽  
Helene Kretzmer ◽  
Arne Sahm ◽  
Simon S McDade ◽  
Steve Hoffmann ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Target Genes ◽  
De Novo ◽  
Therapeutic Interventions ◽  
Gene Regulatory ◽  
Recognition Motifs ◽  
Gene Expression Studies ◽  
Search Approach ◽  
Transcription Factor P53

The transcription factor p53 is the best-known tumor suppressor, but its sibling p63 is a master regulator of epidermis development and a key oncogenic driver in squamous cell carcinomas (SCC). Despite multiple gene expression studies becoming available, the limited overlap of reported p63-dependent genes has made it difficult to decipher the p63 gene regulatory network. Particularly, analyses of p63 response elements differed substantially among the studies. To address this intricate data situation, we provide an integrated resource that enables assessing the p63-dependent regulation of any human gene of interest. We use a novel iterative de novo motif search approach in conjunction with extensive ChIP-seq data to achieve a precise global distinction between p53-and p63-binding sites, recognition motifs, and potential co-factors. We integrate these data with enhancer:gene associations to predict p63 target genes and identify those that are commonly de-regulated in SCC representing candidates for prognosis and therapeutic interventions.

scholarly journals GRAND: a database of gene regulatory network models across human conditions

2021 ◽  
Author(s):  
Marouen Ben Guebila ◽  
Camila M Lopes-Ramos ◽  
Deborah Weighill ◽  
Abhijeet Rajendra Sonawane ◽  
Rebekka Burkholz ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Small Molecule ◽  
Regulatory Network ◽  
Web Application ◽  
Target Genes ◽  
Network Models ◽  
Response To Therapy ◽  
Specific Gene ◽  
Phenotypic Information ◽  
Gene Regulatory

Abstract Gene regulation plays a fundamental role in shaping tissue identity, function, and response to perturbation. Regulatory processes are controlled by complex networks of interacting elements, including transcription factors, miRNAs and their target genes. The structure of these networks helps to determine phenotypes and can ultimately influence the development of disease or response to therapy. We developed GRAND (https://grand.networkmedicine.org) as a database for computationally-inferred, context-specific gene regulatory network models that can be compared between biological states, or used to predict which drugs produce changes in regulatory network structure. The database includes 12 468 genome-scale networks covering 36 human tissues, 28 cancers, 1378 unperturbed cell lines, as well as 173 013 TF and gene targeting scores for 2858 small molecule-induced cell line perturbation paired with phenotypic information. GRAND allows the networks to be queried using phenotypic information and visualized using a variety of interactive tools. In addition, it includes a web application that matches disease states to potentially therapeutic small molecule drugs using regulatory network properties.

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