scholarly journals Identification of new therapeutic targets in CRLF2-overexpressing B-ALL through discovery of TF-gene regulatory interactions

2019 ◽  
Author(s):  
Sana Badri ◽  
Beth Carella ◽  
Priscillia Lhoumaud ◽  
Dayanne M. Castro ◽  
Claudia Skok Gibbs ◽  
...  
Keyword(s):  
Regulatory Network ◽  
Therapeutic Targets ◽  
Genetic Alterations ◽  
Patient Specific ◽  
Regulatory Interactions ◽  
Transcriptional Regulatory ◽  
Gene Regulatory ◽  
New Therapeutic Targets ◽  
Tf Gene ◽  
The Impact

ABSTRACTAlthough genetic alterations are initial drivers of disease, aberrantly activated transcriptional regulatory programs are often responsible for the maintenance and progression of cancer. CRLF2-overexpression in B-ALL patients leads to activation of JAK-STAT, PI3K and ERK/MAPK signaling pathways and is associated with poor outcome. Although inhibitors of these pathways are available, there remains the issue of treatment-associated toxicities, thus it is important to identify new therapeutic targets. Using a network inference approach, we reconstructed a B-ALL specific transcriptional regulatory network to evaluate the impact of CRLF2-overexpression on downstream regulatory interactions.Comparing RNA-seq from CRLF2-High and other B-ALL patients (CRLF2-Low), we defined a CRLF2-High gene signature. Patient-specific chromatin accessibility was interrogated to identify altered putative regulatory elements that could be linked to transcriptional changes. To delineate these regulatory interactions, a B-ALL cancer-specific regulatory network was inferred using 868 B-ALL patient samples from the NCI TARGET database coupled with priors generated from ATAC-seq peak TF-motif analysis. CRISPRi, siRNA knockdown and ChIP-seq of nine TFs involved in the inferred network were analyzed to validate predicted TF-gene regulatory interactions.In this study, a B-ALL specific regulatory network was constructed using ATAC-seq derived priors. Inferred interactions were used to identify differential patient-specific transcription factor activities predicted to control CRLF2-High deregulated genes, thereby enabling identification of new potential therapeutic targets.

scholarly journals Prediction of genome-wide effects of single nucleotide variants on transcription factor binding

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Sebastian Carrasco Pro ◽  
Katia Bulekova ◽  
Brian Gregor ◽  
Adam Labadorf ◽  
Juan Ignacio Fuxman Bass
Keyword(s):  
Binding Sites ◽  
Cancer Type ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Regulatory Regions ◽  
Genome Wide ◽  
Transcriptional Regulatory ◽  
Gene Regulatory ◽  
The Impact ◽  
The Relationship

Abstract Single nucleotide variants (SNVs) located in transcriptional regulatory regions can result in gene expression changes that lead to adaptive or detrimental phenotypic outcomes. Here, we predict gain or loss of binding sites for 741 transcription factors (TFs) across the human genome. We calculated ‘gainability’ and ‘disruptability’ scores for each TF that represent the likelihood of binding sites being created or disrupted, respectively. We found that functional cis-eQTL SNVs are more likely to alter TF binding sites than rare SNVs in the human population. In addition, we show that cancer somatic mutations have different effects on TF binding sites from different TF families on a cancer-type basis. Finally, we discuss the relationship between these results and cancer mutational signatures. Altogether, we provide a blueprint to study the impact of SNVs derived from genetic variation or disease association on TF binding to gene regulatory regions.

scholarly journals Modeling a gene regulatory network of EMT hybrid states for mouse embryonic skin cells

2019 ◽  
Author(s):  
Dan Ramirez ◽  
Vivek Kohar ◽  
Ataur Katebi ◽  
Mingyang Lu
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Epithelial Mesenchymal Transition ◽  
Expression Patterns ◽  
Mesenchymal Transition ◽  
Hybrid State ◽  
Regulatory Interactions ◽  
Skin Cells ◽  
Embryonic Skin ◽  
Gene Regulatory

AbstractEpithelial-mesenchymal transition (EMT) plays a crucial role in embryonic development and tumorigenesis. Although EMT has been extensively studied with both computational and experimental methods, the gene regulatory mechanisms governing the transition are not yet well understood. Recent investigations have begun to better characterize the complex phenotypic plasticity underlying EMT using a computational systems biology approach. Here, we analyzed recently published single-cell RNA sequencing data from E9.5 to E11.5 mouse embryonic skin cells and identified the gene expression patterns of both epithelial and mesenchymal phenotypes, as well as a clear hybrid state. By integrating the scRNA-seq data and gene regulatory interactions from the literature, we constructed a gene regulatory network model governing the decision-making of EMT in the context of the developing mouse embryo. We simulated the network using a recently developed mathematical modeling method, named RACIPE, and observed three distinct phenotypic states whose gene expression patterns can be associated with the epithelial, hybrid, and mesenchymal states in the scRNA-seq data. Additionally, the model is in agreement with published results on the composition of EMT phenotypes and regulatory networks. We identified Wnt signaling as a major pathway in inducing the EMT and its role in driving cellular state transitions during embryonic development. Our findings demonstrate a new method of identifying and incorporating tissue-specific regulatory interactions into gene regulatory network modeling.Author SummaryEpithelial-mesenchymal transition (EMT) is a cellular process wherein cells become disconnected from their surroundings and acquire the ability to migrate through the body. EMT has been observed in biological contexts including development, wound healing, and cancer, yet the regulatory mechanisms underlying it are not well understood. Of particular interest is a purported hybrid state, in which cells can retain some adhesion to their surroundings but also show mesenchymal traits. Here, we examine the prevalence and composition of the hybrid state in the context of the embryonic mouse, integrating gene regulatory interactions from published experimental results as well as from the specific single cell RNA sequencing dataset of interest. Using mathematical modeling, we simulated a regulatory network based on these sources and aligned the simulated phenotypes with those in the data. We identified a hybrid EMT phenotype and revealed the inducing effect of Wnt signaling on EMT in this context. Our regulatory network construction process can be applied beyond EMT to illuminate the behavior of any biological phenomenon occurring in a specific context, allowing better identification of therapeutic targets and further research directions.

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 Odd-paired controls frequency doubling in Drosophila segmentation by altering the pair-rule gene regulatory network

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Erik Clark ◽  
Michael Akam
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Frequency Doubling ◽  
Drosophila Embryo ◽  
Anteroposterior Patterning ◽  
Regulatory Interactions ◽  
Pair Rule Gene ◽  
Gene Regulatory ◽  
Pair Rule

The Drosophila embryo transiently exhibits a double-segment periodicity, defined by the expression of seven 'pair-rule' genes, each in a pattern of seven stripes. At gastrulation, interactions between the pair-rule genes lead to frequency doubling and the patterning of 14 parasegment boundaries. In contrast to earlier stages of Drosophila anteroposterior patterning, this transition is not well understood. By carefully analysing the spatiotemporal dynamics of pair-rule gene expression, we demonstrate that frequency-doubling is precipitated by multiple coordinated changes to the network of regulatory interactions between the pair-rule genes. We identify the broadly expressed but temporally patterned transcription factor, Odd-paired (Opa/Zic), as the cause of these changes, and show that the patterning of the even-numbered parasegment boundaries relies on Opa-dependent regulatory interactions. Our findings indicate that the pair-rule gene regulatory network has a temporally modulated topology, permitting the pair-rule genes to play stage-specific patterning roles.

scholarly journals Perturbation-based gene regulatory network inference to unravel oncogenic mechanisms

2019 ◽  
Author(s):  
Daniel Morgan ◽  
Matthew Studham ◽  
Andreas Tjärnberg ◽  
Holger Weishaupt ◽  
Fredrik J. Swartling ◽  
...  
Keyword(s):  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Network Inference ◽  
Gene Regulatory Network Inference ◽  
Validation Data ◽  
Regulatory Interactions ◽  
Link Type ◽  
Gene Regulatory ◽  
Novel Interactions

AbstractThe gene regulatory network (GRN) of human cells encodes mechanisms to ensure proper functioning. However, if this GRN is dysregulated, the cell may enter into a disease state such as cancer. Understanding the GRN as a system can therefore help identify novel mechanisms underlying disease, which can lead to new therapies. Reliable inference of GRNs is however still a major challenge in systems biology.To deduce regulatory interactions relevant to cancer, we applied a recent computational inference framework to data from perturbation experiments in squamous carcinoma cell line A431. GRNs were inferred using several methods, and the false discovery rate was controlled by the NestBoot framework. We developed a novel approach to assess the predictiveness of inferred GRNs against validation data, despite the lack of a gold standard. The best GRN was significantly more predictive than the null model, both in crossvalidated benchmarks and for an independent dataset of the same genes under a different perturbation design. It agrees with many known links, in addition to predicting a large number of novel interactions from which a subset was experimentally validated. The inferred GRN captures regulatory interactions central to cancer-relevant processes and thus provides mechanistic insights that are useful for future cancer research.Data available at GSE125958Inferred GRNs and inference statistics available at https://dcolin.shinyapps.io/CancerGRN/ Software available at https://bitbucket.org/sonnhammergrni/genespider/src/BFECV/Author SummaryCancer is the second most common cause of death globally, and although cancer treatments have improved in recent years, we need to understand how regulatory mechanisms are altered in cancer to combat the disease efficiently. By applying gene perturbations and inference of gene regulatory networks to 40 genes known or suspected to have a role in cancer due to interactions with the oncogene MYC, we deduce their underlying regulatory interactions. Using a recent computational framework for inference together with a novel method for cross validation, we infer a reliable regulatory model of this system in a completely data driven manner, not reliant on literature or priors. The novel interactions add to the understanding of the progressive oncogenic regulatory process and may provide new targets for therapy.

scholarly journals Abiotic stress induced miRNA-TF-gene regulatory network: A structural perspective

Genomics ◽  
2020 ◽  
Vol 112 (1) ◽  
pp. 412-422 ◽  
Author(s):  
Rinku Sharma ◽  
Shashankaditya Upadhyay ◽  
Basharat Bhat ◽  
Garima Singh ◽  
Sudeepto Bhattacharya ◽  
...  
Keyword(s):  
Abiotic Stress ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Gene Regulatory ◽  
Tf Gene ◽  
Structural Perspective

scholarly journals The Impact of Gene Expression Variation on the Robustness and Evolvability of a Developmental Gene Regulatory Network

PLoS Biology ◽  
2013 ◽  
Vol 11 (10) ◽  
pp. e1001696 ◽  
Author(s):  
David A. Garfield ◽  
Daniel E. Runcie ◽  
Courtney C. Babbitt ◽  
Ralph Haygood ◽  
William J. Nielsen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Gene Expression Variation ◽  
Developmental Gene ◽  
Expression Variation ◽  
Gene Regulatory ◽  
The Impact

scholarly journals Predicting genotype-specific gene regulatory networks

2021 ◽  
Author(s):  
Deborah Weighill ◽  
Marouen Ben Guebila ◽  
Kimberly Glass ◽  
John Quackenbush ◽  
John Platig
Keyword(s):  
Gene Expression ◽  
Gene Regulatory Network ◽  
Gene Regulatory Networks ◽  
Genetic Variants ◽  
Regulatory Network ◽  
Regulatory Networks ◽  
Specific Gene ◽  
Disease Etiology ◽  
Gene Regulatory ◽  
The Impact

AbstractThe majority of disease-associated genetic variants are thought to have regulatory effects, including the disruption of transcription factor (TF) binding and the alteration of downstream gene expression. Identifying how a person’s genotype affects their individual gene regulatory network has the potential to provide important insights into disease etiology and to enable improved genotype-specific disease risk assessments and treatments. However, the impact of genetic variants is generally not considered when constructing gene regulatory networks. To address this unmet need, we developed EGRET (Estimating the Genetic Regulatory Effect on TFs), which infers a genotype-specific gene regulatory network (GRN) for each individual in a study population by using message passing to integrate genotype-informed TF motif predictions - derived from individual genotype data, the predicted effects of variants on TF binding and gene expression, and TF motif predictions - with TF protein-protein interactions and gene expression. Comparing EGRET networks for two blood-derived cell lines identified genotype-associated cell-line specific regulatory differences which were subsequently validated using allele-specific expression, chromatin accessibility QTLs, and differential TF binding from ChIP-seq. In addition, EGRET GRNs for three cell types across 119 individuals captured regulatory differences associated with disease in a cell-type-specific manner. Our analyses demonstrate that EGRET networks can capture the impact of genetic variants on complex phenotypes, supporting a novel fine-scale stratification of individuals based on their genetic background. EGRET is available through the Network Zoo R package (netZooR v0.9; netzoo.github.io).

scholarly journals Gene regulatory network analysis of Trichoderma reesei grown on glucose-lactose mixtures suggests links between development and cellulase production

2020 ◽  
Author(s):  
Aurélie Pirayre ◽  
Laurent Duval ◽  
Corinne Blugeon ◽  
Cyril Firmo ◽  
Sandrine Perrin ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Gene Regulatory Network ◽  
Trichoderma Reesei ◽  
Regulatory Network ◽  
Cellulase Production ◽  
Biofuel Production ◽  
Batch Mode ◽  
Lactose Concentration ◽  
Gene Regulatory ◽  
The Impact

Abstract Background: The degradation of cellulose and hemicellulose molecules into simpler sugars such as glucose is part of the second generation biofuel production process. Hydrolysis of lignocellulosic substrates is usually performed by enzymes produced and secreted by the fungus Trichoderma reesei . Studies identifying transcription factors involved in the regulation of cellulase production have been conducted but no overview of the whole regulation network is available. A transcriptomic approach with mixtures of glucose and lactose, used as a substrate for cellulase induction, was used to help us decipher missing parts in the network.Results: Experimental results confirmed the impact of sugar mixture on the enzymatic cocktail composition. The transcriptomic study shows a temporal regulation of the main transcription factors and a lactose concentration impact on the transcriptional profile. A gene regulatory network built using the BRANE Cut software reveals three sub-networks related to i) a positive correlation between lactose concentration and cellulase production, ii) a particular dependence of the lactose onto the β-glucosidase regulation and iii) a negative regulation of the development process and growth.Conclusions: This work is the first investigating a transcriptomic study regarding the effects of pure and mixed carbon sources in a fed-batch mode. Our study expose a co-orchestration of xyr1 , clr2 and ace3 for cellulase and hemicellulase induction and production, a fine regulation of the β-glucosidase and a decrease of growth in favor of cellulase production. These conclusions provide us with potential targets for further genetic engineering leading to better cellulase-producing strains.

scholarly journals The G-box transcriptional regulatory code in Arabidopsis

2017 ◽  
Author(s):  
Daphne Ezer ◽  
Samuel JK Shepherd ◽  
Anna Brestovitsky ◽  
Patrick Dickinson ◽  
Sandra Cortijo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Regulatory Network ◽  
Environmental Stressors ◽  
Sequence Motifs ◽  
Transcription Pattern ◽  
Expression Of Genes ◽  
Transcriptional Regulatory ◽  
Flanking Sequences ◽  
Gene Regulatory

ABSTRACTPlants have significantly more transcription factor (TF) families than animals and fungi, and plant TF families tend to contain more genes—these expansions are linked to adaptation to environmental stressors (1, 2). Many TF family members bind to similar or identical sequence motifs, such as G-boxes (CACGTG), so it is difficult to predict regulatory relationships. We determine that the flanking sequences near G-boxes help determine in vitro specificity, but that this is insufficient to predict the transcription pattern of genes near G-boxes. Therefore, we construct a gene regulatory network that identifies the set of bZIPs and bHLHs that are most predictive of the gene expression of genes downstream of perfect G-boxes. This network accurately predicts transcriptional patterns and reconstructs known regulatory subnetworks. Finally, we present Ara-BOX-cis (araboxcis.org), a website that provides interactive visualisations of the G-box regulatory network, a useful resource for generating predictions for gene regulatory relations.

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