Author: Noh, Heeju; Shoemaker, Jason E; Gunawan, Rudiyanto
Title: Network perturbation analysis of gene transcriptional profiles reveals protein targets and mechanism of action of drugs and influenza A viral infection Document date: 2018_4_6
ID: j80hnhpb_12
Snippet: Protein-gene regulatory network. In ProTINA, the PGRN is a bipartite graph with weighted, directed edges pointing from a protein to a gene (see Figure 1A ). The edges in the PGRN describe the regulation of gene expression by TFs and their protein partners, the molecular targets of interest in this work. The bipartite PGRN above is able to capture feedback loops in the gene transcriptional regulation, even though these loops are not drawn explicit.....
Document: Protein-gene regulatory network. In ProTINA, the PGRN is a bipartite graph with weighted, directed edges pointing from a protein to a gene (see Figure 1A ). The edges in the PGRN describe the regulation of gene expression by TFs and their protein partners, the molecular targets of interest in this work. The bipartite PGRN above is able to capture feedback loops in the gene transcriptional regulation, even though these loops are not drawn explicitly. An example of such a feedback loop is when a protein regulates the expression of its own transcription factor(s). The PGRN is constructed by combining two types of networks, namely the TF-gene network and PIN. For the construction of human cell type-specific PGRNs, we relied on the Regulatory Circuit resource that provides 394 cell type and tissue-specific TF-gene interactions (33) . More specifically, for the analysis of the NCI-DREAM drug synergy, genotoxic compound study, and influenza A viral infection study datasets, we used the TF-gene networks of human lymphoma cells, pleomorphic hepatocellular carcinoma cells, and epithelium lung cancer cells, respectively. We included only TF-gene interactions with a Regulatory Circuit confidence score greater than 0.1. The confidence score indicates the normalized promoter activity level in a given cell type (0: not active, 1: maximally active) (33) . For the analysis of mouse pancreatic cell dataset, we obtained the mouse pancreatic TF-gene interactions from CellNet (34) . In the construction of the PGRNs, any TF-gene interactions involving unmeasured genes were excluded. In summary, the TFgene network for human lymphoma, hepatocellular carcinoma cell, and epithelium lung cancer cell lines included 31 392 edges pointing from 515 TFs to 5153 genes, 3868 edges pointing from 413 TFs to 953 genes, and 42 145 edges pointing from 515 TFs to 7125 genes, respectively. The mouse pancreatic PGRN included 2922 edges, involving 95 TFs and 588 genes.
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