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 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.

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 Developmental transcriptomics of the brittle star Amphiura filiformis reveals gene regulatory network rewiring in echinoderm larval skeleton evolution

2017 ◽  
Author(s):  
David Dylus ◽  
Liisa M. Blowes ◽  
Anna Czarkwiani ◽  
Maurice R. Elphick ◽  
Paola Oliveri
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
De Novo ◽  
Sea Urchins ◽  
Brittle Star ◽  
Stages Of Development ◽  
Sequence Alignments ◽  
Amphiura Filiformis ◽  
Gene Regulatory ◽  
Network Rewiring

ABSTRACTAmongst the echinoderms the class Ophiuroidea is of particular interest for its phylogenetic position, ecological importance, developmental and regenerative biology. However, compared to other echinoderms, notably echinoids (sea urchins), relatively little is known about developmental changes in gene expression in ophiuroids. To address this issue we have generated and assembled a large RNAseq data set of four key stages of development in the brittle star Amphiura filiformis and a de novo reference transcriptome of comparable quality to that of a model echinoderm - the sea urchin Strongyloncentrotus purpuratus. Furthermore, we provide access to the new data via a web interface: http://www.echinonet.eu/shiny/Amphiura_filiformis/. With a focus on skeleton development, we have identified highly conserved genes associated with the development of a biomineralized skeleton. We also identify important class-specific characters, including the independent duplication of the msp130 class of genes in different echinoderm classes and the unique occurrence of spicule matrix (sm) genes in echinoids. Using a new quantification pipeline for our de novo transcriptome, validated with other methodologies, we find major differences between brittle stars and sea urchins in the temporal expression of many transcription factor genes. This divergence in developmental regulatory states is more evident in early stages of development when cell specification begins, than when cells initiate differentiation. Our findings indicate that there has been a high degree of gene regulatory network rewiring in the evolution of echinoderm larval development.Data DepositionsAll sequence reads are available at Genbank SRR4436669 - SRR4436674. Any sequence alignments used are available by the corresponding author upon request.

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 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.

scholarly journals Construction and Clarification of Dynamic Gene Regulatory Network of Cancer Cell Cycle via Microarray Data

2006 ◽  
Vol 2 ◽  
pp. 117693510600200 ◽  
Author(s):  
Cheng-Wei Li ◽  
Yung-Hsiang Chu ◽  
Bor-Sen Chen
Keyword(s):  
Cell Cycle ◽  
Hela Cell ◽  
Dynamic Model ◽  
Gene Regulatory Network ◽  
Cancer Cell ◽  
Microarray Data ◽  
Regulatory Network ◽  
Target Genes ◽  
Expression Profiles ◽  
Gene Regulatory

Background Cell cycle is an important clue to unravel the mechanism of cancer cells. Recently, expression profiles of cDNA microarray data of Cancer cell cycle are available for the information of dynamic interactions among Cancer cell cycle related genes. Therefore, it is more appealing to construct a dynamic model for gene regulatory network of Cancer cell cycle to gain more insight into the infrastructure of gene regulatory mechanism of cancer cell via microarray data. Results Based on the gene regulatory dynamic model and microarray data, we construct the whole dynamic gene regulatory network of Cancer cell cycle. In this study, we trace back upstream regulatory genes of a target gene to infer the regulatory pathways of the gene network by maximum likelihood estimation method. Finally, based on the dynamic regulatory network, we analyze the regulatory abilities and sensitivities of regulatory genes to clarify their roles in the mechanism of Cancer cell cycle. Conclusions Our study presents a systematically iterative approach to discern and characterize the transcriptional regulatory network in Hela cell cycle from the raw expression profiles. The transcription regulatory network in Hela cell cycle can also be confirmed by some experimental reviews. Based on our study and some literature reviews, we can predict and clarify the E2F target genes in G1/S phase, which are crucial for regulating cell cycle progression and tumorigenesis. From the results of the network construction and literature confirmation, we infer that MCM4, MCM5, CDC6, CDC25A, UNG and E2F2 are E2F target genes in Hela cell cycle.

Application of a Hidden Markov Model in Identifying Target Genes in Breast Cancer

2020 ◽  
pp. 523-532
Author(s):  
Parisa Torkaman
Keyword(s):  
Breast Cancer ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Target Genes ◽  
Hidden Markov ◽  
Transition Probability ◽  
Number Of Patients ◽  
Gene Regulatory

Breast cancer is one of the most common malignant cancers among women with increasing number of patients. Gene regulatory network and identifying target genes for cancer treatment, and reducing breast cancer death rates is of great importance medically. This study aims to model gene regulatory network of breast cancer using hidden Markov model which greatly aids doctors in early diagnosis and faster treatment of breast cancer using identification of target genes. In this study, gene expressions of $206$ patients diagnosed with four subtypes of breast cancer including, Basal, Her2, LumA, LumB, were obtained from the Cancer Genome Atlas (TCGA). $8$ genes with the verified interaction among them were investigated by hidden Markov model of gene regulatory network and target genes. with the results of transition probability matrix, FADD, TNFRSF10B, CASP8 are the target genes in the mentioned cancer subtypes so that genes that their transmit probabilities are more than an initial value of $0.125$ are regulatory genes and transmit matrix identifies the probability of the mentioned cancers regarding gene expression level.

scholarly journals MicroRNAs organize intrinsic variation into stem cell states

2020 ◽  
Vol 117 (12) ◽  
pp. 6942-6950 ◽  
Author(s):  
Meenakshi Chakraborty ◽  
Sofia Hu ◽  
Erica Visness ◽  
Marco Del Giudice ◽  
Andrea De Martino ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Target Genes ◽  
Embryonic Stem ◽  
State Transitions ◽  
Environmental Signals ◽  
Morphogen Gradients ◽  
Developmental Gene Expression ◽  
Gene Regulatory

Pluripotent embryonic stem cells (ESCs) contain the potential to form a diverse array of cells with distinct gene expression states, namely the cells of the adult vertebrate. Classically, diversity has been attributed to cells sensing their position with respect to external morphogen gradients. However, an alternative is that diversity arises in part from cooption of fluctuations in the gene regulatory network. Here we find ESCs exhibit intrinsic heterogeneity in the absence of external gradients by forming interconverting cell states. States vary in developmental gene expression programs and display distinct activity of microRNAs (miRNAs). Notably, miRNAs act on neighborhoods of pluripotency genes to increase variation of target genes and cell states. Loss of miRNAs that vary across states reduces target variation and delays state transitions, suggesting variable miRNAs organize and propagate variation to promote state transitions. Together these findings provide insight into how a gene regulatory network can coopt variation intrinsic to cell systems to form robust gene expression states. Interactions between intrinsic heterogeneity and environmental signals may help achieve developmental outcomes.

scholarly journals Gene-regulatory network analysis of ankylosing spondylitis with a single-cell chromatin accessible assay

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Haiyan Yu ◽  
Hongwei Wu ◽  
Fengping Zheng ◽  
Chengxin Zhu ◽  
Lianghong Yin ◽  
...  
Keyword(s):  
T Cells ◽  
Ankylosing Spondylitis ◽  
Single Cell ◽  
Signaling Pathway ◽  
Gene Regulatory Network ◽  
Cd8 T Cells ◽  
Regulatory Network ◽  
Target Genes ◽  
Epigenetic Therapy ◽  
Gene Regulatory

Abstract A detailed understanding of the gene-regulatory network in ankylosing spondylitis (AS) is vital for elucidating the mechanisms of AS pathogenesis. Assaying transposase-accessible chromatin in single cell sequencing (scATAC-seq) is a suitable method for revealing such networks. Thus, scATAC-seq was applied to define the landscape of active regulatory DNA in AS. As a result, there was a significant change in the percent of CD8+ T cells in PBMCs, and 37 differentially accessible transcription factor (TF) motifs were identified. T cells, monocytes-1 and dendritic cells were found to be crucial for the IL-17 signaling pathway and TNF signaling pathway, since they had 73 potential target genes regulated by 8 TF motifs with decreased accessibility in AS. Moreover, natural killer cells were involved in AS by increasing the accessibility to TF motifs TEAD1 and JUN to induce cytokine-cytokine receptor interactions. In addition, CD4+ T cells and CD8+ T cells may be vital for altering host immune functions through increasing the accessibility of TF motifs NR1H4 and OLIG (OLIGI and OLIG2), respectively. These results explain clear gene regulatory variation in PBMCs from AS patients, providing a foundational framework for the study of personal regulomes and delivering insights into epigenetic therapy.

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