The promoters of human cell cycle genes integrate signals from two tumor suppressive pathways during cellular transformation
Yuval Tabach, Michael Milyavsky, Igor Shats, Ran Brosh, Or Zuk, Assif Yitzhaky, Roberto Mantovani, Eytan Domany, Varda Rotter Yitzhak Pilpel
Abstract
Deciphering regulatory events that drive malignant transformation represents a major challenge for systems biology. Here we analyzed genome-wide transcription profiling of an in-vitro transformation process. We focused on a cluster of genes whose expression levels increased as a function of p53 and p16INK4A tumor suppressors inactivation. This cluster predominantly consists of cell cycle genes and constitutes a signature of a diversity of cancers. By linking expression profiles of the genes in the cluster with the dynamic behavior of p53 and p16INK4A, we identified a promoter architecture that integrates signals from the two tumor suppressive channels and that maps their activity onto distinct levels of expression of the cell cycle genes, which in turn, correspond to different cellular proliferation rates. Taking components of the mitotic spindle as an example, we experimentally verified our predictions that p53-mediated transcriptional repression of several of these novel targets is dependent on the activities of p21, NFY and E2F. Our study demonstrates how a well-controlled transformation process allows linking between gene expression, promoter architecture and activity of upstream signaling molecules.
Create a lesson
Related papers
Systematic pathway comparison on the powerset of rule-based biochemical systems
Anne-Susann Abel, Sissel Banke, Erika M. Herrera Machado et al.
Uncovering Cellular Resolution in scRNAseq via Unbiased Cell and Gene Network Analysis
Olga lanzetta, Luisa Cutillo, Bailey Andrew et al.
DigiPhen: a new paradigm for building predictive models of biological systems
H. Steven Wiley, Angela Cintolesi, Niaz Bahar Chowdhury et al.
Motional Degrees of Freedom in Network Hamiltonian Models
Peng Huang, Elizabeth M. Diessner, Carter T. Butts
Metabolic Network Properties: Comprehensive Analysis Across Domains
José Antônio Pellizzaro, Daniel Gamermann, Julian Triana Dopico
Reducing Boolean Networks via Analysis of Dynamic Network Subgraph Behavior
Soodabeh Zakeri, Mohieddin Jafari