Inference of RhoGAP/GTPase regulation using single-cell morphological data from a combinatorial RNAi screen
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Nir-2010-Inference of RhoGAP.pdf
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Author(s)
Nir, Oaz
Bakal, Chris
Perrimon, Norbert
Berger, Bonnie
Date Issued
February 2010
Journal
Genome Research
Publisher
Cold Spring Harbor Laboratory Press
Citation
Nir, O. et al. “Inference of RhoGAP/GTPase Regulation Using Single-cell Morphological Data from a Combinatorial RNAi Screen.” Genome Research 20.3 (2010): 372–380. Copyright © 2010 by Cold Spring Harbor Laboratory Press
Version
Final published version
Abstract
Biological networks are highly complex systems, consisting largely of enzymes that act as molecular switches to activate/inhibit downstream targets via post-translational modification. Computational techniques have been developed to perform signaling network inference using some high-throughput data sources, such as those generated from transcriptional and proteomic studies, but comparable methods have not been developed to use high-content morphological data, which are emerging principally from large-scale RNAi screens, to these ends. Here, we describe a systematic computational framework based on a classification model for identifying genetic interactions using high-dimensional single-cell morphological data from genetic screens, apply it to RhoGAP/GTPase regulation in Drosophila, and evaluate its efficacy. Augmented by knowledge of the basic structure of RhoGAP/GTPase signaling, namely, that GAPs act directly upstream of GTPases, we apply our framework for identifying genetic interactions to predict signaling relationships between these proteins. We find that our method makes mediocre predictions using only RhoGAP single-knockdown morphological data, yet achieves vastly improved accuracy by including original data from a double-knockdown RhoGAP genetic screen, which likely reflects the redundant network structure of RhoGAP/GTPase signaling. We consider other possible methods for inference and show that our primary model outperforms the alternatives. This work demonstrates the fundamental fact that high-throughput morphological data can be used in a systematic, successful fashion to identify genetic interactions and, using additional elementary knowledge of network structure, to infer signaling relations.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Mathematics
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Creative Commons Attribution Non-Commercial
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DOI of Published Version
https://doi.org/10.1101/gr.100248.109
