Protein interaction network topology uncovers melanogenesis regulatory network components within functional genomics datasets
- Equal contributors
1 Department of Biological Chemistry, University of California, Irvine, CA 92697-1700, USA
2 Department of Computer Science, University of California, Irvine, CA 92697-3435, USA
3 Department of Dermatology, University of California, Irvine, CA 92697-2400, USA
4 Department of Computing, Imperial College London SW7 2AZ, UK
BMC Systems Biology 2010, 4:84 doi:10.1186/1752-0509-4-84Published: 15 June 2010
RNA-mediated interference (RNAi)-based functional genomics is a systems-level approach to identify novel genes that control biological phenotypes. Existing computational approaches can identify individual genes from RNAi datasets that regulate a given biological process. However, currently available methods cannot identify which RNAi screen "hits" are novel components of well-characterized biological pathways known to regulate the interrogated phenotype. In this study, we describe a method to identify genes from RNAi datasets that are novel components of known biological pathways. We experimentally validate our approach in the context of a recently completed RNAi screen to identify novel regulators of melanogenesis.
In this study, we utilize a PPI network topology-based approach to identify targets within our RNAi dataset that may be components of known melanogenesis regulatory pathways. Our computational approach identifies a set of screen targets that cluster topologically in a human PPI network with the known pigment regulator Endothelin receptor type B (EDNRB). Validation studies reveal that these genes impact pigment production and EDNRB signaling in pigmented melanoma cells (MNT-1) and normal melanocytes.
We present an approach that identifies novel components of well-characterized biological pathways from functional genomics datasets that could not have been identified by existing statistical and computational approaches.