Fine-Scale Dissection of Functional Protein Network Organization by Statistical Network Analysis

Revealing organizational principles of biological networks is an important goal of systems biology. In this study, we sought to analyze the dynamic organizational principles within the protein interaction network by studying the characteristics of individual neighborhoods of proteins within the network based on their gene expression as well as protein-protein interaction patterns. By clustering proteins into distinct groups based on their neighborhood gene expression characteristics, we identify several significant trends in the dynamic organization of the protein interaction network. We show that proteins with distinct neighborhood gene expression characteristics are positioned in specific localities in the protein interaction network thereby playing specific roles in the dynamic network connectivity. Remarkably, our analysis reveals a neighborhood characteristic that corresponds to the most centrally located group of proteins within the network. Further, we show that the connectivity pattern displayed by this group is consistent with the notion of “rich club connectivity” in complex networks. Importantly, our findings are largely reproducible in networks constructed using independent and different datasets.     

Use of Data-Biased Random Walks on Graphs for the Retrieval of Context-Specific Networks from Genomic Data

Extracting network-based functional relationships within genomic datasets is an important challenge in the computational analysis of large-scale data. Although many methods, both public and commercial, have been developed, the problem of identifying networks of interactions that are most relevant to the given input data still remains an open issue. Here, we have leveraged the method of random walks on graphs as a powerful platform for scoring network components based on simultaneous assessment of the experimental data as well as local network connectivity. Using this method, NetWalk, we can calculate distribution of Edge Flux values associated with each interaction in the network, which reflects the relevance of interactions based on the experimental data. We show that network-based analyses of genomic data are simpler and more accurate using NetWalk than with some of the currently employed methods. We also present NetWalk analysis of microarray gene expression data from MCF7 cells exposed to different doses of doxorubicin, which reveals a switch-like pattern in the p53 regulated network in cell cycle arrest and apoptosis. Our analyses demonstrate the use of NetWalk as a valuable tool in generating high-confidence hypotheses from high-content genomic data.     

Comprehensive Mapping of the Human Kinome to Epidermal Growth Factor Receptor Signaling

Disregulation of epidermal growth factor receptor (EGFR) signaling directly promotes bypass of proliferation and survival restraints in a high frequency of epithelia-derived cancer. As such, much effort is currently focused on decoding the molecular architecture supporting EGFR activation and function. Here, we have leveraged high throughput reverse phase protein lysate arrays, with a sensitive fluorescent nanocrystal-based phosphoprotein detection assay, together with large scale siRNA-mediated loss of function to execute a quantitative interrogation of all elements of the human kinome supporting EGF-dependent signaling. This screening platform has captured multiple novel contributions of diverse protein kinases to modulation of EGFR signal generation, signal amplitude, and signal duration. As examples, the prometastatic SNF1/AMPK-related kinase hormonally upregulated Neu kinase was found to support EGFR activation in response to ligand binding, whereas the enigmatic kinase MGC16169 selectively supports coupling of active EGFR to ERK1/2 regulation. Of note, the receptor tyrosine kinase MERTK and the pyrimidine kinase UCK1 were both found to be required for surface accumulation of EGFR and subsequent pathway activation in multiple cancer cell backgrounds and may represent new targets for therapeutic intervention.     

Host Modulators of H1N1 Cytopathogenicity

Influenza A virus infects 5-20% of the population annually, resulting in ～35,000 deaths and significant morbidity. Current treatments include vaccines and drugs that target viral proteins. However, both of these approaches have limitations, as vaccines require yearly development and the rapid evolution of viral proteins gives rise to drug resistance. In consequence additional intervention strategies, that target host factors required for the viral life cycle, are under investigation. Here we employed arrayed whole-genome siRNA screening strategies to identify cell-autonomous molecular components that are subverted to support H1N1 influenza A virus infection of human bronchial epithelial cells. Integration across relevant public data sets exposed druggable gene products required for epithelial cell infection or required for viral proteins to deflect host cell suicide checkpoint activation. Pharmacological inhibition of representative targets, RGGT and CHEK1, resulted in significant protection against infection of human epithelial cells by the A/WS/33 virus. In addition, chemical inhibition of RGGT partially protected against H5N1 and the 2009 H1N1 pandemic strain. The observations reported here thus contribute to an expanding body of studies directed at decoding vulnerabilities in the command and control networks specified by influenza virulence factors.     

The glucose‐deprivation network counteracts lapatinib‐induced toxicity in resistant ErbB2‐positive breast cancer cells

Dynamic interactions between intracellular networks regulate cellular homeostasis and responses to perturbations. Targeted therapy is aimed at perturbing oncogene addiction pathways in cancer, however, development of acquired resistance to these drugs is a significant clinical problem. A network‐based computational analysis of global gene expression data from matched sensitive and acquired drug‐resistant cells to lapatinib, an EGFR/ErbB2 inhibitor, revealed an increased expression of the glucose deprivation response network, including glucagon signaling, glucose uptake, gluconeogenesis and unfolded protein response in the resistant cells. Importantly, the glucose deprivation response markers correlated significantly with high clinical relapse rates in ErbB2‐positive breast cancer patients. Further, forcing drug‐sensitive cells into glucose deprivation rendered them more resistant to lapatinib. Using a chemical genomics bioinformatics mining of the CMAP database, we identified drugs that specifically target the glucose deprivation response networks to overcome the resistant phenotype and reduced survival of resistant cells. This study implicates the chronic activation of cellular compensatory networks in response to targeted therapy and suggests novel combinations targeting signaling and metabolic networks in tumors with acquired resistance.     

Patterns of human gene expression variance show strong associations with signaling network hierarchy

Background  

Understanding organizational principles of cellular networks is one of the central goals of systems biology. Although much has been learnt about gene expression programs under specific conditions, global patterns of expressional variation (EV) of genes and their relationship to cellular functions and physiological responses is poorly understood.     

Inherited DNA Repair Defects Disrupt the Structure and Function of Human Skin

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