Mapping the human phosphatome on growth pathways

Molecular Systems Biology

Francesca Sacco¹  Pier Federico Gherardini¹  Serena Paoluzi¹  Julio Saez-Rodriguez²  Manuela Helmer-Citterich¹  Antonella Ragnini-Wilson¹  Luisa Castagnoli¹ 

[1] Department of Biology, University of Rome ‘Tor Vergata’, Rome, Italy;EMBL-EBI, Hinxton, UK

关键词: cancer;    computational biology;    functional genomics;    imaging;    modeling;   

DOI  :  10.1038/msb.2012.36

来源: Wiley

【 摘 要 】

Abstract

Large-scale siRNA screenings allow linking the function of poorly characterized genes to phenotypic readouts. According to this strategy, genes are associated with a function of interest if the alteration of their expression perturbs the phenotypic readouts. However, given the intricacy of the cell regulatory network, the mapping procedure is low resolution and the resulting models provide little mechanistic insights. We have developed a new strategy that combines multiparametric analysis of cell perturbation with logic modeling to achieve a more detailed functional mapping of human genes onto complex pathways. A literature-derived optimized model is used to infer the cell activation state following upregulation or downregulation of the model entities. By matching this signature with the experimental profile obtained in the high-throughput siRNA screening it is possible to infer the target of each protein, thus defining its ‘entry point’ in the network. By this novel approach, 41 phosphatases that affect key growth pathways were identified and mapped onto a human epithelial cell-specific growth model, thus providing insights into the mechanisms underlying their function.

Synopsis

Phosphatases control cell growth by a variety of mechanisms. A novel strategy is presented that combines multiparametric analysis of cell perturbations with logic modeling to achieve a detailed mapping of human phosphatase function on growth pathways.

• siRNA-mediated downregulation of 298 phosphatase and phosphatase-related genes coupled to automated microscopy was used to characterize their impact on key growth pathways.
• In parallel, a literature-derived signed directed network was derived and optimized by training with experimental data.
• The resulting logic-based growth model was used to infer the cell state upon perturbation of each signaling node and compare it with the profiles obtained upon phosphatase perturbation.
• Mapping of 67% of the protein phosphatase onto the growth model shows that phosphatases are key modulators of growth pathways and affect cell-cycle progression.
• This novel approach is general and enables to efficiently map proteins onto complex pathways.

【 授权许可】

CC BY-NC-SA   
Copyright © 2012 EMBO and Macmillan Publishers Limited

Creative Commons Attribution License, which permits distribution, and reproduction in any medium, provided the original author and source are credited. This license does not permit commercial exploitation without specific permission.