Context-specific transcriptional regulatory network inference from global gene expression maps using double two-way t-tests

The rapid increase of genome-wide datasets on gene expression, chromatin states, and transcription factor (TF) binding locations offers an exciting opportunity to interpret the information encoded in genomes and epigenomes. This task can be challenging as it requires joint modeling of context-specific activation of cis-regulatory elements (REs) and the effects on transcription of associated regulatory factors. To meet this challenge, we propose a statistical approach based on paired expression and chromatin accessibility (PECA) data across diverse cellular contexts. In our approach, we model (i) the localization to REs of chromatin regulators (CRs) based on their interaction with sequence-specific TFs, (ii) the activation of REs due to CRs that are localized to them, and (iii) the effect of TFs bound to activated REs on the transcription of target genes (TGs). The transcriptional regulatory network inferred by PECA provides a detailed view of how trans- and cis-regulatory elements work together to affect gene expression in a context-specific manner. We illustrate the feasibility of this approach by analyzing paired expression and accessibility data from the mouse Encyclopedia of DNA Elements (ENCODE) and explore various applications of the resulting model.

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Functional Modules Analysis and Hub Gene Prognostic Values Evaluation Based on Co-Expression Network in Gastric Cancer

10.21203/rs.3.rs-600479/v2 ◽

2021 ◽

Author(s):

Yujia Liu ◽

Xiaoping Hu ◽

Zongfu Pan ◽

Yuchen Jiang ◽

Dandan Guo ◽

...

Keyword(s):

Gene Expression ◽

Gastric Cancer ◽

Regulatory Network ◽

Molecular Mechanisms ◽

Transcriptional Regulatory Network ◽

Receptor Interaction ◽

Ppi Network ◽

Hub Gene ◽

Transcriptional Regulatory ◽

Key Nodes

Abstract Background: Gastric cancer is one of the most common fatal disease worldwide, but its mechanism and therapeutic targets are still unclear. In this study, we have analyzed the differences in gene modules and key pathways in gastric cancer patients, then elaborated the mechanism and effective treatment of gastric cancer with microarray data from the gene expression omnibus(GEO) database. Methods: GEO2R tools were used to identify differential expression genes (DEGs), String database was employed to construct a protein-protein interaction (PPI) network. We imported the PPI network into the Cytoscape software to find key nodes, and employed statistical approach of MCODE to cluster genes. After that the ClueGO was used to enrich and annotate the pathways of key modules. To investigate the relationship between the upstream regulator and hub genes, the transcriptional regulatory network was built based on TFCAT database. Results: 63 characteristic genes of gastric cancer are involved in regulation of ECM-receptor interaction, focal adhesion and protein digestion and absorption. SPARC, FN1, BGN and COL1A2 are four key nodes relating to tumor proliferation and metastasis, and their expression were strongly associated with poor survival (p<0.05). 13 transcription factors including PRRX1 have remarkable changes in gastric cancer, which may play a key role in hub gene regulation. Conclusions: The present study defined the gene expression characteristics and transcriptional regulatory network that promote our understanding of the molecular mechanisms underlying the development of gastric cancer, and might provide new insights into targeted therapy and prognostic markers for the personalized treatment of gastric cancer.

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A fuzzy logic approach to infer transcriptional regulatory network in saccharomyces cerevisiae using promoter site prediction and gene expression pattern recognition

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence) ◽

10.1109/cec.2008.4631021 ◽

2008 ◽

Author(s):

Cheng-Long Chuang ◽

Chung-Ming Chen ◽

Grace S. Shieh ◽

Joe-Air Jiang

Keyword(s):

Gene Expression ◽

Saccharomyces Cerevisiae ◽

Pattern Recognition ◽

Fuzzy Logic ◽

Regulatory Network ◽

Transcriptional Regulatory Network ◽

Gene Expression Pattern ◽

Transcriptional Regulatory ◽

Fuzzy Logic Approach ◽

Logic Approach

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Genetic and pharmacological inactivation of adenosine A2A receptor reveals an Egr-2-mediated transcriptional regulatory network in the mouse striatum

Physiological Genomics ◽

10.1152/physiolgenomics.00068.2005 ◽

2005 ◽

Vol 23 (1) ◽

pp. 89-102 ◽

Cited By ~ 11

Author(s):

Liqun Yu ◽

Peter M. Haverty ◽

Juliana Mariani ◽

Yumei Wang ◽

Hai-Ying Shen ◽

...

Keyword(s):

Gene Expression ◽

Regulatory Network ◽

Molecular Mechanisms ◽

Transcriptional Regulatory Network ◽

Expression Profiles ◽

Mrna Level ◽

Gene Expression Profiles ◽

Bioinformatics Analyses ◽

Mouse Striatum ◽

Transcriptional Regulatory

The adenosine A2A receptor (A2AR) is highly expressed in the striatum, where it modulates motor and emotional behaviors. We used both microarray and bioinformatics analyses to compare gene expression profiles by genetic and pharmacological inactivation of A2AR and inferred an A2AR-controlled transcription network in the mouse striatum. A comparison between vehicle (VEH)-treated A2AR knockout (KO) mice (A2AR KO-VEH) and wild-type (WT) mice (WT-VEH) revealed 36 upregulated genes that were partially mimicked by treatment with SCH-58261 (SCH; an A2AR antagonist) and 54 downregulated genes that were not mimicked by SCH treatment. We validated the A2AR as a specific drug target for SCH by comparing A2AR KO-SCH and A2AR KO-VEH groups. The unique downregulation effect of A2AR KO was confirmed by comparing A2AR KO-SCH with WT-SCH gene groups. The distinct striatal gene expression profiles induced by A2AR KO and SCH should provide clues to the molecular mechanisms underlying the different phenotypes observed after genetic and pharmacological inactivation of A2AR. Furthermore, bioinformatics analysis discovered that Egr-2 binding sites were statistically overrepresented in the proximal promoters of A2AR KO-affected genes relative to the unaffected genes. This finding was further substantiated by the demonstration that the Egr-2 mRNA level increased in the striatum of both A2AR KO and SCH-treated mice and that striatal Egr-2 binding activity in the promoters of two A2AR KO-affected genes was enhanced in A2AR KO mice as assayed by chromatin immunoprecipitation. Taken together, these results strongly support the existence of an Egr-2-directed transcriptional regulatory network controlled by striatal A2ARs.

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Identification of transcriptional regulatory network associated with response of host epithelial cells to SARS-CoV-2

Scientific Reports ◽

10.1038/s41598-021-03309-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Chen Su ◽

Simon Rousseau ◽

Amin Emad

Keyword(s):

Gene Expression ◽

Epithelial Cells ◽

Regulatory Network ◽

Transcriptional Regulatory Network ◽

Organ Damage ◽

Host Cells ◽

Regulatory Mechanisms ◽

Transcriptional Regulatory ◽

Transcriptional Regulatory Mechanisms ◽

The Impact

AbstractIdentification of transcriptional regulatory mechanisms and signaling networks involved in the response of host cells to infection by SARS-CoV-2 is a powerful approach that provides a systems biology view of gene expression programs involved in COVID-19 and may enable the identification of novel therapeutic targets and strategies to mitigate the impact of this disease. In this study, our goal was to identify a transcriptional regulatory network that is associated with gene expression changes between samples infected by SARS-CoV-2 and those that are infected by other respiratory viruses to narrow the results on those enriched or specific to SARS-CoV-2. We combined a series of recently developed computational tools to identify transcriptional regulatory mechanisms involved in the response of epithelial cells to infection by SARS-CoV-2, and particularly regulatory mechanisms that are specific to this virus when compared to other viruses. In addition, using network-guided analyses, we identified kinases associated with this network. The results identified pathways associated with regulation of inflammation (MAPK14) and immunity (BTK, MBX) that may contribute to exacerbate organ damage linked with complications of COVID-19. The regulatory network identified herein reflects a combination of known hits and novel candidate pathways supporting the novel computational pipeline presented herein to quickly narrow down promising avenues of investigation when facing an emerging and novel disease such as COVID-19.

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Leveraging chromatin accessibility for transcriptional regulatory network inference in T Helper 17 Cells

10.1101/292987 ◽

2018 ◽

Cited By ~ 3

Author(s):

Emily R. Miraldi ◽

Maria Pokrovskii ◽

Aaron Watters ◽

Dayanne M. Castro ◽

Nicholas De Veaux ◽

...

Keyword(s):

Gene Expression ◽

Regulatory Networks ◽

Network Inference ◽

Transcriptional Regulatory Network ◽

Indirect Evidence ◽

T Helper ◽

Motif Analysis ◽

T Helper 17 ◽

Transcriptional Regulatory

AbstractTranscriptional regulatory networks (TRNs) provide insight into cellular behavior by describing interactions between transcription factors (TFs) and their gene targets. The Assay for Transposase Accessible Chromatin (ATAC)-seq, coupled with transcription-factor motif analysis, provides indirect evidence of chromatin binding for hundreds of TFs genome-wide. Here, we propose methods for TRN inference in a mammalian setting, using ATAC-seq data to influence gene expression modeling. We rigorously test our methods in the context of T Helper Cell Type 17 (Th17) differentiation, generating new ATAC-seq data to complement existing Th17 genomic resources (plentiful gene expression data, TF knock-outs and ChIP-seq experiments). In this resource-rich mammalian setting, our extensive benchmarking provides quantitative, genome-scale evaluation of TRN inference combining ATAC-seq and RNA-seq data. We refine and extend our previous Th17 TRN, using our new TRN inference methods to integrate all Th17 data (gene expression, ATAC-seq, TF KO, ChIP-seq). We highlight newly discovered roles for individual TFs and groups of TFs (“TF-TF modules”) in Th17 gene regulation. Given the popularity of ATAC-seq, which provides high-resolution with low sample input requirements, we anticipate that application of our methods will improve TRN inference in new mammalian systems, especially in vivo, for cells directly from humans and animal models.

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