Transcriptional Regulatory Networks Activated by PI3K and ERK Transduced Growth Signals in Human Glioblastoma Cells  

作　　者：PeterM.Haverty 翁志萍 UllaHansen 

机构地区：[1]BioinformaticsProgram,BostonUniversity,44CummingtonStreet,Boston,MA02215,U.S.A. [2]BioinformaticsProgram,BostonUniversity,44CummingtonStreet,Boston,MA02215,U.S.A.／／BiomedicalEngineeringDepartment,BostonUniversity,44CummingtonStreet,Boston,MA02215,U.S.A. [3]BioinformaticsProgram,BostonUniversity,44CummingtonStreet,Boston,MA02215,U.S.A.／／BiologyDepartment,BostonUniversity,44CummingtonStreet,Boston,MA02215,U.S.A.

出　　处：《Journal of Computer Science & Technology》2005年第4期439-445,共7页计算机科学技术学报（英文版）

摘　　要：Determining how cells regulate their transcriptional response toextracellular signals is key to the understanding of complex eukaryotic systems. This study wasinitiated with the goals of furthering the study of mammalian transcriptional regulation andanalyzing the relative benefits of related computational methodologies. One dataset available forsuch an analysis involved gene expression profiling of the early growth factor response to plateletderived growth factor (PDGF) in a human glioblastoma cell line; this study differentiated geneswhose expression was regulated by signaling through the phosphoinositide-3-kinase (PI3K) versus theextracellular-signal regulated kinase (ERK) pathways. We have compared the inferred transcriptionfactors from this previous study with additional predictions of regulatory transcription factorsusing two alternative promoter sequence analysis techniques. This comparative analysis, in which thealgorithms predict overlapping, although not identical, sets of factors, argues for meticulousbenchmarking of promoter sequence analysis methods to determine the positive and negative attributesthat contribute to their varying results. Finally, we inferred transcriptional regulatory networksderiving from various signaling pathways using the CARRIE program suite. These networks not onlyincluded previously described transcriptional features of the response to growth signals, but alsopredicted new regulatory features for the propagation and modulation of the growth signal.Determining how cells regulate their transcriptional response toextracellular signals is key to the understanding of complex eukaryotic systems. This study wasinitiated with the goals of furthering the study of mammalian transcriptional regulation andanalyzing the relative benefits of related computational methodologies. One dataset available forsuch an analysis involved gene expression profiling of the early growth factor response to plateletderived growth factor (PDGF) in a human glioblastoma cell line; this study differentiated geneswhose expression was regulated by signaling through the phosphoinositide-3-kinase (PI3K) versus theextracellular-signal regulated kinase (ERK) pathways. We have compared the inferred transcriptionfactors from this previous study with additional predictions of regulatory transcription factorsusing two alternative promoter sequence analysis techniques. This comparative analysis, in which thealgorithms predict overlapping, although not identical, sets of factors, argues for meticulousbenchmarking of promoter sequence analysis methods to determine the positive and negative attributesthat contribute to their varying results. Finally, we inferred transcriptional regulatory networksderiving from various signaling pathways using the CARRIE program suite. These networks not onlyincluded previously described transcriptional features of the response to growth signals, but alsopredicted new regulatory features for the propagation and modulation of the growth signal.

关 键 词：PI3K ERK PDGF transcriptional regulatory network CIS-ELEMENT 

分 类 号：TN911.7[电子电信—通信与信息系统]