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

Woolf, Peter J., and Yixin Wang. A fuzzy logic approach to analyzing gene expression data. Physiol Genomics 3: 9–15, 2000.—We have developed a novel algorithm for analyzing gene expression data. This algorithm uses fuzzy logic to transform expression values into qualitative descriptors that can be evaluated by using a set of heuristic rules. In our tests we designed a model to find triplets of activators, repressors, and targets in a yeast gene expression data set. For the conditions tested, the predictions made by the algorithm agree well with experimental data in the literature. The algorithm can also assist in determining the function of uncharacterized proteins and is able to detect a substantially larger number of transcription factors than could be found at random. This technology extends current techniques such as clustering in that it allows the user to generate a connected network of genes using only expression data.

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

Bioinformatics ◽

10.1093/bioinformatics/bts434 ◽

2012 ◽

Vol 28 (18) ◽

pp. 2325-2332 ◽

Cited By ~ 9

Author(s):

Jianlong Qi ◽

Tom Michoel

Keyword(s):

Gene Expression ◽

Regulatory Network ◽

Network Inference ◽

Transcriptional Regulatory Network ◽

Global Gene Expression ◽

Transcriptional Regulatory ◽

Context Specific

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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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Spatial organization of the transcriptional regulatory network of Saccharomyces cerevisiae

10.1101/509224 ◽

2019 ◽

Author(s):

Dong-Qing Sun ◽

Liu Tian ◽

Bin-Guang Ma

Keyword(s):

Saccharomyces Cerevisiae ◽

Regulatory Network ◽

Spatial Organization ◽

Target Genes ◽

Transcriptional Regulatory Network ◽

3D Structure ◽

Three Dimensional ◽

3D Genome ◽

Transcriptional Regulatory ◽

Four Levels

AbstractTranscriptional regulatory network (TRN) is a directed complex network composed of all regulatory interactions between transcription factors and corresponding target genes. Recently, the three-dimensional (3D) genomics studies have shown that the 3D structure of the genome makes a difference to the regulation of gene transcription, which provides us with a novel perspective. In this study, we constructed the TRN of the budding yeast Saccharomyces cerevisiae and placed it in the context of 3D genome model. We analyzed the spatial organization of the yeast TRN on four levels: global feature, central nodes, hierarchical structure and network motifs. Our results suggested that the TRN of S. cerevisiae presents an optimized structure in space to adapt to functional requirement.

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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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