scholarly journals Regulation of PD1 signaling is associated with prognosis in glioblastoma multiforme

2021 ◽  
Author(s):  
Camila Lopes-Ramos ◽  
Tatiana Belova ◽  
Tess Brunner ◽  
John Quackenbush ◽  
Marieke L. Kuijjer
Keyword(s):  
Gene Regulatory Networks ◽  
Transcriptional Repression ◽  
Regulatory Networks ◽  
Treatment Options ◽  
Neoadjuvant Treatment ◽  
The Cancer Genome Atlas ◽  
Therapeutic Interventions ◽  
Short Term ◽  
Term Survival ◽  
Gene Regulatory

Glioblastoma is an aggressive cancer of the brain and spine. While analysis of glioblastoma ‘omics data has somewhat improved our understanding of the disease, it has not led to direct improvement in patient survival. Cancer survival is often characterized by differences in expression of particular genes, but the mechanisms that drive these differences are generally unknown. We therefore set out to model the regulatory mechanisms that associate with glioblastoma survival. We inferred individual patient gene regulatory networks using data from two different expression platforms from The Cancer Genome Atlas (n=522 and 431). We performed a comparative network analysis between patients with long- and short-term survival, correcting for patient age, sex, and neoadjuvant treatment status. We identified seven pathways associated with survival, all of which were involved in immune system signaling. Differential regulation of PD1 signaling was validated in an independent dataset from the German Glioma Network (n=70). We found that transcriptional repression of genes in this pathway—for which treatment options are available—was lost in short-term survivors and that this was independent of mutation burden and only weakly associated with T-cell infiltrate. These results provide a new way to stratify glioblastoma patients that uses network features as biomarkers to predict survival, and identify new potential therapeutic interventions, thus underscoring the value of analyzing gene regulatory networks in individual cancer patients.

scholarly journals Computational Methodologies for Analyzing, Modeling and Controlling Gene Regulatory Networks

2010 ◽  
Vol 2 ◽  
pp. BECB.S5594 ◽  
Author(s):  
Zahra Zamani ◽  
Amirhossein Hajihosseini ◽  
Ali Masoudi-Nejad
Keyword(s):  
Molecular Biology ◽  
Computational Methods ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
The Other ◽  
Therapeutic Interventions ◽  
Control Network ◽  
Deep Insight ◽  
Paper Briefly ◽  
Gene Regulatory

Molecular biology focuses on genes and their interactions at the transcription, regulation and protein level. Finding genes that cause certain behaviors can make therapeutic interventions more effective. Although biological tools can extract the genes and perform some analyses, without the help of computational methods, deep insight of the genetic function and its effects will not occur. On the other hand, complex systems can be modeled by networks, introducing the main data as nodes and the links in-between as the transactions occurring within the network. Gene regulatory networks are examples that are modeled and analyzed in order to gain insight of their exact functions. Since a cell's specific functionality is greatly determined by the genes it expresses, translation or the act of converting mRNA to proteins is highly regulated by the control network that directs cellular activities. This paper briefly reviews the most important computational methods for analyzing, modeling and controlling the gene regulatory networks.

scholarly journals Cell-free gene-regulatory network engineering with synthetic transcription factors

2019 ◽  
Vol 116 (13) ◽  
pp. 5892-5901 ◽  
Author(s):  
Zoe Swank ◽  
Nadanai Laohakunakorn ◽  
Sebastian J. Maerkl
Keyword(s):  
Transcription Factors ◽  
High Throughput ◽  
Gene Regulatory Networks ◽  
Protein Interactions ◽  
Transcriptional Repression ◽  
Regulatory Networks ◽  
De Novo ◽  
Mechanistic Modeling ◽  
Free System ◽  
Gene Regulatory

Gene-regulatory networks are ubiquitous in nature and critical for bottom-up engineering of synthetic networks. Transcriptional repression is a fundamental function that can be tuned at the level of DNA, protein, and cooperative protein–protein interactions, necessitating high-throughput experimental approaches for in-depth characterization. Here, we used a cell-free system in combination with a high-throughput microfluidic device to comprehensively study the different tuning mechanisms of a synthetic zinc-finger repressor library, whose affinity and cooperativity can be rationally engineered. The device is integrated into a comprehensive workflow that includes determination of transcription-factor binding-energy landscapes and mechanistic modeling, enabling us to generate a library of well-characterized synthetic transcription factors and corresponding promoters, which we then used to build gene-regulatory networks de novo. The well-characterized synthetic parts and insights gained should be useful for rationally engineering gene-regulatory networks and for studying the biophysics of transcriptional regulation.

scholarly journals Master Regulators, Regulatory Networks, and Pathways of Glioblastoma Subtypes

2014 ◽  
Vol 13s3 ◽  
pp. CIN.S14027 ◽  
Author(s):  
Serdar Bozdag ◽  
Aiguo Li ◽  
Mehmet Baysan ◽  
Howard A. Fine
Keyword(s):  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Gene Set Enrichment ◽  
Master Regulators ◽  
Cancer Genome Atlas ◽  
Gene Regulatory ◽  
Comprehensive Study

Glioblastoma multiforme (GBM) is the most common malignant brain tumor. GBM samples are classified into subtypes based on their transcriptomic and epigenetic profiles. Despite numerous studies to better characterize GBM biology, a comprehensive study to identify GBM subtype-specific master regulators, gene regulatory networks, and pathways is missing. Here, we used FastMEDUSA to compute master regulators and gene regulatory networks for each GBM subtype. We also ran Gene Set Enrichment Analysis and Ingenuity Pathway Analysis on GBM expression dataset from The Cancer Genome Atlas Project to compute GBM- and GBM subtype-specific pathways. Our analysis was able to recover some of the known master regulators and pathways in GBM as well as some putative novel regulators and pathways, which will aide in our understanding of the unique biology of GBM subtypes.

scholarly journals Cell-free gene regulatory network engineering with synthetic transcription factors

2018 ◽  
Author(s):  
Zoe Swank ◽  
Nadanai Laohakunakorn ◽  
Sebastian J. Maerkl
Keyword(s):  
Transcription Factors ◽  
High Throughput ◽  
Gene Regulatory Networks ◽  
Protein Interactions ◽  
Transcriptional Repression ◽  
Regulatory Networks ◽  
De Novo ◽  
Mechanistic Modeling ◽  
Free System ◽  
Gene Regulatory

AbstractGene regulatory networks are ubiquitous in nature and critical for bottom-up engineering of synthetic networks. Transcriptional repression is a fundamental function that can be tuned at the level of DNA, protein, and cooperative protein – protein interactions, necessitating high-throughput experimental approaches for in-depth characterization. Here we used a cell-free system in combination with a high-throughput microfluidic device to comprehensively study the different tuning mechanisms of a synthetic zinc-finger repressor library, whose affinity and cooperativity can be rationally engineered. The device is integrated into a comprehensive workflow that includes determination of transcription factor binding energy landscapes and mechanistic modeling, enabling us to generate a library of well-characterized synthetic transcription factors and corresponding promoters, which we then used to build gene regulatory networks de novo. The well-characterized synthetic parts and insights gained should be useful for rationally engineering gene regulatory networks and for studying the biophysics of transcriptional regulation.

scholarly journals GRNdb: decoding the gene regulatory networks in diverse human and mouse conditions

2020 ◽  
Vol 49 (D1) ◽  
pp. D97-D103
Author(s):  
Li Fang ◽  
Yunjin Li ◽  
Lu Ma ◽  
Qiyue Xu ◽  
Fei Tan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Large Scale ◽  
Gene Expression Regulation ◽  
Expression Regulation ◽  
The Cancer Genome Atlas ◽  
Gene Regulatory ◽  
Human And Mouse

Abstract Gene regulatory networks (GRNs) formed by transcription factors (TFs) and their downstream target genes play essential roles in gene expression regulation. Moreover, GRNs can be dynamic changing across different conditions, which are crucial for understanding the underlying mechanisms of disease pathogenesis. However, no existing database provides comprehensive GRN information for various human and mouse normal tissues and diseases at the single-cell level. Based on the known TF-target relationships and the large-scale single-cell RNA-seq data collected from public databases as well as the bulk data of The Cancer Genome Atlas and the Genotype-Tissue Expression project, we systematically predicted the GRNs of 184 different physiological and pathological conditions of human and mouse involving >633 000 cells and >27 700 bulk samples. We further developed GRNdb, a freely accessible and user-friendly database (http://www.grndb.com/) for searching, comparing, browsing, visualizing, and downloading the predicted information of 77 746 GRNs, 19 687 841 TF-target pairs, and related binding motifs at single-cell/bulk resolution. GRNdb also allows users to explore the gene expression profile, correlations, and the associations between expression levels and the patient survival of diverse cancers. Overall, GRNdb provides a valuable and timely resource to the scientific community to elucidate the functions and mechanisms of gene expression regulation in various conditions.

scholarly journals Dynamics and predicted drug response of a gene network linking dedifferentiation with β-catenin dysfunction in hepatocellular carcinoma

2018 ◽  
Author(s):  
Claude Gérard ◽  
Mickaël Di-Luoffo ◽  
Léolo Gonay ◽  
Stefano Caruso ◽  
Gabrielle Couchy ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
The Cancer Genome Atlas ◽  
Pharmacological Agents ◽  
Distinct Cell ◽  
Cancer Genome Atlas ◽  
Gene Regulatory ◽  
Using Data ◽  
Insight Into

AbstractAlterations of individual genes variably affect development of hepatocellular carcinoma (HCC), prompting the need to characterize the function of tumor-promoting genes in the context of gene regulatory networks (GRN). Here, we identify a GRN which functionally links LIN28B-dependent dedifferentiation with dysfunction of CTNNB1 (β-CATENIN). LIN28B and CTNNB1 form a functional GRN with SMARCA4 (BRG1), Let-7b, SOX9, TP53 and MYC. GRN activity is detected in HCC and gastrointestinal cancers; it negatively correlates with HCC prognosis and contributes to a transcriptomic profile typical of the proliferative class of HCC. Using data from The Cancer Genome Atlas and from transcriptomic, transfection and mouse transgenic experiments, we generated and validated a quantitative mathematical model of the GRN. The model predicts how the expression of GRN components changes when the expression of another GRN member varies or is inhibited by a pharmacological drug. The dynamics of GRN component expression reveal distinct cell states that can switch reversibly in normal condition, and irreversibly in HCC. We conclude that identification and modelling of the GRN provides insight into prognosis, mechanisms of tumor-promoting genes and response to pharmacological agents in HCC.

scholarly journals Modeling the dynamics of Let-7-coupled gene regulatory networks linking cell proliferation to malignant transformation

2019 ◽  
Author(s):  
Claude Gérard ◽  
Frédéric Lemaigre ◽  
Didier Gonze
Keyword(s):  
Cell Cycle ◽  
Data Analysis ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
The Cancer Genome Atlas ◽  
Cancer Type ◽  
Transcriptomic Data ◽  
Transformed Cell ◽  
Cell State ◽  
Gene Regulatory

AbstractThe microRNA Let-7 controls the expression of proteins that belong to two distinct gene regulatory networks, namely a cyclin-dependent kinases (Cdks) network driving the cell cycle and a cell transformation network which can undergo an epigenetic switch between a non-transformed and a malignant transformed cell state.Using mathematical modeling and transcriptomic data analysis, we here investigate how Let-7 controls the cdk-dependent cell cycle network, and how it couples the latter with the transformation network. We also determine whether the two networks can be combined into a larger entity that impacts on cancer progression.Our analysis shows that the switch from a quiescent to a cycling state depends on the relative levels of Let-7 and several cell cycle activators. Numerical simulations further indicate that the Let-7-coupled cell cycle and transformation networks control each other, and our model identifies key players for this mutual control. Transcriptomic data analysis from the The Cancer Genome Atlas (TCGA) suggest that the two networks are activated in cancer, in particular in gastrointestinal cancers, and that the activation levels vary significantly among patients affected with a same cancer type. Our mathematical model, when applied to a heterogeneous cell population, suggests that heterogeneity among tumors results from stochastic switches between a non-transformed cell state with low proliferative capability and a transformed cell state with high proliferative property. The model further predicts that Let-7 may reduce tumor heterogeneity by decreasing the occurrence of stochastic switches towards a transformed, proliferative cell state.In conclusion, we identified the key components responsible for the qualitative dynamics of two GRNs interconnected by Let-7. The two GRNs are heterogeneously involved in several cancers, thereby stressing the need to consider patient’s specific GRN characteristics to optimize therapeutic strategies.

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