scholarly journals Inference of phenotype-relevant transcriptional regulatory networks elucidates cancer type-specific regulatory mechanisms in a pan-cancer study

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Amin Emad ◽  
Saurabh Sinha
Keyword(s):  
Regulatory Networks ◽  
Target Genes ◽  
Graphical Model ◽  
Activity Level ◽  
The Cancer Genome Atlas ◽  
Regulatory Mechanisms ◽  
Cancer Type ◽  
Transcriptional Regulatory Networks ◽  
Cancer Driver ◽  
Transcriptional Regulatory

AbstractReconstruction of transcriptional regulatory networks (TRNs) is a powerful approach to unravel the gene expression programs involved in healthy and disease states of a cell. However, these networks are usually reconstructed independent of the phenotypic (or clinical) properties of the samples. Therefore, they may confound regulatory mechanisms that are specifically related to a phenotypic property with more general mechanisms underlying the full complement of the analyzed samples. In this study, we develop a method called InPheRNo to identify “phenotype-relevant” TRNs. This method is based on a probabilistic graphical model that models the simultaneous effects of multiple transcription factors (TFs) on their target genes and the statistical relationship between the target genes’ expression and the phenotype. Extensive comparison of InPheRNo with related approaches using primary tumor samples of 18 cancer types from The Cancer Genome Atlas reveals that InPheRNo can accurately reconstruct cancer type-relevant TRNs and identify cancer driver TFs. In addition, survival analysis reveals that the activity level of TFs with many target genes could distinguish patients with poor prognosis from those with better prognosis.

scholarly journals Inference of phenotype-relevant transcriptional regulatory networks elucidates cancer type-specific regulatory mechanisms in a pan-cancer study

2018 ◽  
Author(s):  
Amin Emad ◽  
Saurabh Sinha
Keyword(s):  
Gene Expression ◽  
Regulatory Networks ◽  
Target Genes ◽  
Graphical Model ◽  
Activity Level ◽  
The Cancer Genome Atlas ◽  
Regulatory Mechanisms ◽  
Cancer Type ◽  
Transcriptional Regulatory Networks ◽  
Transcriptional Regulatory

ABSTRACTReconstruction of transcriptional regulatory networks (TRNs) is a powerful approach to unravel the gene expression programs involved in healthy and disease states of a cell. However, these networks are usually reconstructed independent of the phenotypic properties of the samples and therefore cannot identify regulatory mechanisms that are related to a phenotypic outcome of interest. In this study, we developed a new method called InPheRNo to identify ‘phenotype-relevant’ transcriptional regulatory networks. This method is based on a probabilistic graphical model whose conditional probability distributions model the simultaneous effects of multiple transcription factors (TFs) on their target genes as well as the statistical relationship between target gene expression and phenotype. Extensive comparison of InPheRNo with related approaches using primary tumor samples of 18 cancer types from The Cancer Genome Atlas revealed that InPheRNo can accurately reconstruct cancer type-relevant TRNs and identify cancer driver TFs. In addition, survival analysis revealed that the activity level of TFs with many target genes could distinguish patients with good prognosis from those with poor prognosis.

scholarly journals Experiment level curation identifies high confidence transcriptional regulatory interactions in neurodevelopment

2021 ◽  
Author(s):  
Eric Ching-Pan Chu ◽  
Alexander Morin ◽  
Tak Hou Calvin Chang ◽  
Tue Nguyen ◽  
Yi-Cheng Tsai ◽  
...  
Keyword(s):  
Nervous System ◽  
Experimental Evidence ◽  
Regulatory Networks ◽  
Large Scale ◽  
Target Genes ◽  
High Confidence ◽  
Transcriptional Regulatory Networks ◽  
Regulatory Interactions ◽  
Transcriptional Regulatory ◽  
Significant Enrichment

To facilitate the development of large-scale transcriptional regulatory networks (TRNs) that may enable in-silico analyses of disease mechanisms, a reliable catalogue of experimentally verified direct transcriptional regulatory interactions (DTRIs) is needed for training and validation. There has been a long history of using low-throughput experiments to validate single DTRIs. Therefore, we hypothesize that a reliable set of DTRIs could be produced by curating the published literature for such evidence. In our survey of previous curation efforts, we identified the lack of details about the quantity and the types of experimental evidence to be a major gap, despite the importance of such details for the identification of bona fide DTRIs. We developed a curation protocol to inspect the published literature for support of DTRIs at the experiment level, focusing on genes important to the development of the mammalian nervous system. We sought to record three types of low-throughput experiments: Transcription factor (TF) perturbation, TF-DNA binding, and TF-reporter assays. Using this protocol, we examined a total of 1,310 papers to assemble a collection of 1,499 unique DTRIs, involving 251 TFs and 825 target genes, many of which were not reported in any other DTRI resource. The majority of DTRIs (965, 64%) were supported by two or more types of experimental evidence and 27% were supported by all three. Of the DTRIs with all three types of evidence, 170 had been tested using primary tissues or cells and 44 had been tested directly in the central nervous system. We used our resource to document research biases among reports towards a small number of well-studied TFs. To demonstrate a use case for this resource, we compared our curation to a previously published high-throughput perturbation screen and found significant enrichment of the curated targets among genes differentially expressed in the developing brain in response to Pax6 deletion. This study demonstrates a proof-of-concept for the assembly of a high confidence DTRI resource in order to support the development of large-scale TRNs.

Structure, evolution and dynamics of transcriptional regulatory networks

2010 ◽  
Vol 38 (5) ◽  
pp. 1155-1178 ◽  
Author(s):  
M. Madan Babu
Keyword(s):  
Regulatory Networks ◽  
Target Genes ◽  
Network Evolution ◽  
Structure Evolution ◽  
Future Research ◽  
Regulation System ◽  
Transcriptional Networks ◽  
Transcriptional Regulatory Networks ◽  
Entire Genome ◽  
Transcriptional Regulatory

The availability of entire genome sequences and the wealth of literature on gene regulation have enabled researchers to model an organism's transcriptional regulation system in the form of a network. In such a network, TFs (transcription factors) and TGs (target genes) are represented as nodes and regulatory interactions between TFs and TGs are represented as directed links. In the present review, I address the following topics pertaining to transcriptional regulatory networks. (i) Structure and organization: first, I introduce the concept of networks and discuss our understanding of the structure and organization of transcriptional networks. (ii) Evolution: I then describe the different mechanisms and forces that influence network evolution and shape network structure. (iii) Dynamics: I discuss studies that have integrated information on dynamics such as mRNA abundance or half-life, with data on transcriptional network in order to elucidate general principles of regulatory network dynamics. In particular, I discuss how cell-to-cell variability in the expression level of TFs could permit differential utilization of the same underlying network by distinct members of a genetically identical cell population. Finally, I conclude by discussing open questions for future research and highlighting the implications for evolution, development, disease and applications such as genetic engineering.

scholarly journals Consensus transcriptional regulatory networks of coronavirus-infected human cells

2020 ◽  
Author(s):  
Scott A Ochsner ◽  
Rudolf T Pillich ◽  
Neil J McKenna
Keyword(s):  
Signaling Pathways ◽  
Regulatory Networks ◽  
Data Exchange ◽  
Epithelial To Mesenchymal Transition ◽  
Target Genes ◽  
Transcriptional Regulatory Networks ◽  
Mesenchymal Transition ◽  
Human Genes ◽  
Transcriptional Regulatory ◽  
Genes Encoding

AbstractEstablishing consensus around the transcriptional interface between coronavirus (CoV) infection and human cellular signaling pathways can catalyze the development of novel anti-CoV therapeutics. Here, we used publicly archived transcriptomic datasets to compute consensus regulatory signatures, or consensomes, that rank human genes based on their rates of differential expression in MERS-CoV (MERS), SARS-CoV-1 (SARS1) and SARS-CoV-2 (SARS2)-infected cells. Validating the CoV consensomes, we show that high confidence transcriptional targets (HCTs) of CoV infection intersect with HCTs of signaling pathway nodes with known roles in CoV infection. Among a series of novel use cases, we gather evidence for hypotheses that SARS2 infection efficiently represses E2F family target genes encoding key drivers of DNA replication and the cell cycle; that progesterone receptor signaling antagonizes SARS2-induced inflammatory signaling in the airway epithelium; and that SARS2 HCTs are enriched for genes involved in epithelial to mesenchymal transition. The CoV infection consensomes and HCT intersection analyses are freely accessible through the Signaling Pathways Project knowledgebase, and as Cytoscape-style networks in the Network Data Exchange repository.

scholarly journals Mechanistic Modeling of Gene Regulation and Metabolism Identifies Potential Targets for Hepatocellular Carcinoma

2020 ◽  
Vol 11 ◽  
Author(s):  
Renliang Sun ◽  
Yizhou Xu ◽  
Hang Zhang ◽  
Qiangzhen Yang ◽  
Ke Wang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regulatory Networks ◽  
Target Genes ◽  
Therapeutic Targets ◽  
Normal Liver ◽  
Metabolic Model ◽  
Cancer Genome ◽  
Mechanistic Modeling ◽  
The Cancer Genome Atlas ◽  
Transcriptional Regulatory

Hepatocellular carcinoma (HCC) is the predominant form of liver cancer and has long been among the top three cancers that cause the most deaths worldwide. Therapeutic options for HCC are limited due to the pronounced tumor heterogeneity. Thus, there is a critical need to study HCC from a systems point of view to discover effective therapeutic targets, such as through the systematic study of disease perturbation in both regulation and metabolism using a unified model. Such integration makes sense for cancers as it links one of the dominant physiological features of cancers (growth, which is driven by metabolic networks) with the primary available omics data source, transcriptomics (which is systematically integrated with metabolism through the regulatory-metabolic network model). Here, we developed an integrated transcriptional regulatory-metabolic model for HCC molecular stratification and the prediction of potential therapeutic targets. To predict transcription factors (TFs) and target genes affecting tumorigenesis, we used two algorithms to reconstruct the genome-scale transcriptional regulatory networks for HCC and normal liver tissue. which were then integrated with corresponding constraint-based metabolic models. Five key TFs affecting cancer cell growth were identified. They included the regulator CREB3L3, which has been associated with poor prognosis. Comprehensive personalized metabolic analysis based on models generated from data of liver HCC in The Cancer Genome Atlas revealed 18 genes essential for tumorigenesis in all three subtypes of patients stratified based on the non-negative matrix factorization method and two other genes (ACADSB and CMPK1) that have been strongly correlated with lower overall survival subtype. Among these 20 genes, 11 are targeted by approved drugs for cancers or cancer-related diseases, and six other genes have corresponding drugs being evaluated experimentally or investigationally. The remaining three genes represent potential targets. We also validated the stratification and prognosis results by an independent dataset of HCC cohort samples (LIRI-JP) from the International Cancer Genome Consortium database. In addition, microRNAs targeting key TFs and genes were also involved in established cancer-related pathways. Taken together, the multi-scale regulatory-metabolic model provided a new approach to assess key mechanisms of HCC cell proliferation in the context of systems and suggested potential targets.

INFERRING THE REGULATORY INTERACTION MODELS OF TRANSCRIPTION FACTORS IN TRANSCRIPTIONAL REGULATORY NETWORKS

2012 ◽  
Vol 10 (05) ◽  
pp. 1250012 ◽  
Author(s):  
SHERINE AWAD ◽  
NICHOLAS PANCHY ◽  
SEE-KIONG NG ◽  
JIN CHEN
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Gene Expression Data ◽  
Regulatory Networks ◽  
Target Genes ◽  
Expression Data ◽  
Regulatory Interaction ◽  
Transcriptional Regulatory Networks ◽  
Interaction Models ◽  
Transcriptional Regulatory

Living cells are realized by complex gene expression programs that are moderated by regulatory proteins called transcription factors (TFs). The TFs control the differential expression of target genes in the context of transcriptional regulatory networks (TRNs), either individually or in groups. Deciphering the mechanisms of how the TFs control the differential expression of a target gene in a TRN is challenging, especially when multiple TFs collaboratively participate in the transcriptional regulation. To unravel the roles of the TFs in the regulatory networks, we model the underlying regulatory interactions in terms of the TF–target interactions' directions (activation or repression) and their corresponding logical roles (necessary and/or sufficient). We design a set of constraints that relate gene expression patterns to regulatory interaction models, and develop TRIM (Transcriptional Regulatory Interaction Model Inference), a new hidden Markov model, to infer the models of TF–target interactions in large-scale TRNs of complex organisms. Besides, by training TRIM with wild-type time-series gene expression data, the activation timepoints of each regulatory module can be obtained. To demonstrate the advantages of TRIM, we applied it on yeast TRN to infer the TF–target interaction models for individual TFs as well as pairs of TFs in collaborative regulatory modules. By comparing with TF knockout and other gene expression data, we were able to show that the performance of TRIM is clearly higher than DREM (the best existing algorithm). In addition, on an individual Arabidopsis binding network, we showed that the target genes' expression correlations can be significantly improved by incorporating the TF–target regulatory interaction models inferred by TRIM into the expression data analysis, which may introduce new knowledge in transcriptional dynamics and bioactivation.

Systematic genetic analysis of transcription factors to map the fission yeast transcription-regulatory network

2013 ◽  
Vol 41 (6) ◽  
pp. 1696-1700 ◽  
Author(s):  
Gordon Chua
Keyword(s):  
Transcription Factor ◽  
Transcription Factors ◽  
Fission Yeast ◽  
Regulatory Networks ◽  
Target Genes ◽  
Transcriptome Profiling ◽  
Transcriptional Regulatory Networks ◽  
Transcriptional Regulatory ◽  
Transcription Factor Genes ◽  
Almost All

Mapping transcriptional-regulatory networks requires the identification of target genes, binding specificities and signalling pathways of transcription factors. However, the characterization of each transcription factor sufficiently for deciphering such networks remains laborious. The recent availability of overexpression and deletion strains for almost all of the transcription factor genes in the fission yeast Schizosaccharomyces pombe provides a valuable resource to better investigate transcription factors using systematic genetics. In the present paper, I review and discuss the utility of these strain collections combined with transcriptome profiling and genome-wide chromatin immunoprecipitation to identify the target genes of transcription factors.

scholarly journals Transcriptional regulatory networks of tumor-associated macrophages that drive malignancy in mesenchymal glioblastoma

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Jason K. Sa ◽  
Nakho Chang ◽  
Hye Won Lee ◽  
Hee Jin Cho ◽  
Michele Ceccarelli ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Complex Disease ◽  
Target Genes ◽  
Mammalian Target Of Rapamycin ◽  
Differentially Expressed Gene ◽  
Treatment Resistance ◽  
Tumor Associated Macrophages ◽  
Transcriptional Regulatory Networks ◽  
Transcriptional Regulatory

Abstract Background Glioblastoma (GBM) is a complex disease with extensive molecular and transcriptional heterogeneity. GBM can be subcategorized into four distinct subtypes; tumors that shift towards the mesenchymal phenotype upon recurrence are generally associated with treatment resistance, unfavorable prognosis, and the infiltration of pro-tumorigenic macrophages. Results We explore the transcriptional regulatory networks of mesenchymal-associated tumor-associated macrophages (MA-TAMs), which drive the malignant phenotypic state of GBM, and identify macrophage receptor with collagenous structure (MARCO) as the most highly differentially expressed gene. MARCOhigh TAMs induce a phenotypic shift towards mesenchymal cellular state of glioma stem cells, promoting both invasive and proliferative activities, as well as therapeutic resistance to irradiation. MARCOhigh TAMs also significantly accelerate tumor engraftment and growth in vivo. Moreover, both MA-TAM master regulators and their target genes are significantly correlated with poor clinical outcomes and are often associated with genomic aberrations in neurofibromin 1 (NF1) and phosphoinositide 3-kinases/mammalian target of rapamycin/Akt pathway (PI3K-mTOR-AKT)-related genes. We further demonstrate the origination of MA-TAMs from peripheral blood, as well as their potential association with tumor-induced polarization states and immunosuppressive environments. Conclusions Collectively, our study characterizes the global transcriptional profile of TAMs driving mesenchymal GBM pathogenesis, providing potential therapeutic targets for improving the effectiveness of GBM immunotherapy.

Sign in / Sign up

Export Citation Format

Share Document