Inferring Gene-Phenotype Associations via Global Protein Complex Network Propagation

BACKGROUND: Phenotypically similar diseases have been found to be caused by functionally related genes, suggesting a modular organization of the genetic landscape of human diseases that mirrors the modularity observed in biological interaction networks. Protein complexes, as molecular machines that integrate multiple gene products to perform biological functions, express the underlying modular organization of protein-protein interaction networks. As such, protein complexes can be useful for interrogating the networks of phenome and interactome to elucidate gene-phenotype associations of diseases. METHODOLOGY/PRINCIPAL FINDINGS: We proposed a technique called RWPCN (Random Walker on Protein Complex Network) for predicting and prioritizing disease genes. The basis of RWPCN is a protein complex network constructed using existing human protein complexes and protein interaction network. To prioritize candidate disease genes for the query disease phenotypes, we compute the associations between the protein complexes and the query phenotypes in their respective protein complex and phenotype networks. We tested RWPCN on predicting gene-phenotype associations using leave-one-out cross-validation; our method was observed to outperform existing approaches. We also applied RWPCN to predict novel disease genes for two representative diseases, namely, Breast Cancer and Diabetes. CONCLUSIONS/SIGNIFICANCE: Guilt-by-association prediction and prioritization of disease genes can be enhanced by fully exploiting the underlying modular organizations of both the disease phenome and the protein interactome. Our RWPCN uses a novel protein complex network as a basis for interrogating the human phenome-interactome network. As the protein complex network can capture the underlying modularity in the biological interaction networks better than simple protein interaction networks, RWPCN was found to be able to detect and prioritize disease genes better than traditional approaches that used only protein-phenotype associations

LMO4 is an essential mediator of ErbB2/HER2/Neu-induced breast cancer cell cycle progression.

ErbB2/HER2/Neu-overexpressing breast cancers are characterized by poor survival due to high proliferation and metastasis rates and identifying downstream targets of ErbB2 should facilitate developing novel therapies for this disease. Gene expression profiling revealed the transcriptional regulator LIM-only protein 4 (LMO4) is upregulated during ErbB2-induced mouse mammary gland tumorigenesis. Although LMO4 is frequently overexpressed in breast cancer and LMO4-overexpressing mice develop mammary epithelial tumors, the mechanisms involved are unknown. In this study, we report that LMO4 is a downstream target of ErbB2 and PI3K in ErbB2-dependent breast cancer cells. Furthermore, LMO4 silencing reduces proliferation of these cells, inducing a G2/M arrest that was associated with decreased cullin-3, an E3-ubiquitin ligase component important for mitosis. Loss of LMO4 subsequently results in reduced Cyclin D1 and Cyclin E. Further supporting a role for LMO4 in modulating proliferation by regulating cullin-3 expression, we found that LMO4 expression oscillates throughout the cell cycle with maximum expression occurring during G2/M and these changes precede oscillations in cullin-3 levels. LMO4 levels are also highest in high-grade/less differentiated breast cancers, which are characteristically highly proliferative. We conclude that LMO4 is a novel cell cycle regulator with a key role in mediating ErbB2-induced proliferation, a hallmark of ErbB2-positive disease