Revealing static and dynamic modular architecture of the eukaryotic protein interaction network

In an effort to understand the dynamic organization of the protein interaction network and its role in the regulation of cell behavior, positioning of proteins into specific network localities was studied with respect to their expression dynamics. First, we find that constitutively expressed and dynamically co-regulated proteins cluster in distinct functionally specialized network neighborhoods to form static and dynamic functional modules, respectively. Then, we show that whereas dynamic modules are mainly responsible for condition-dependent regulation of cell behavior, static modules provide robustness to the cell against genetic perturbations or protein expression noise, and therefore may act as buffers of evolutionary as well as population variations in cell behavior. Observations in this study refine the previously proposed model of dynamic modularity in the protein interaction network, and propose a link between the evolution of gene expression regulation and biological robustness

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

Myeloid Malignancies with Chromosome 5q Deletions Acquire a Dependency on an Intrachromosomal NF-κB Gene Network

SummaryChromosome 5q deletions (del[5q]) are common in high-risk (HR) myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML); however, the gene regulatory networks that sustain these aggressive diseases are unknown. Reduced miR-146a expression in del(5q) HR MDS/AML and miR-146a−/− hematopoietic stem/progenitor cells (HSPCs) results in TRAF6/NF-κB activation. Increased survival and proliferation of HSPCs from miR-146alow HR MDS/AML is sustained by a neighboring haploid gene, SQSTM1 (p62), expressed from the intact 5q allele. Overexpression of p62 from the intact allele occurs through NF-κB-dependent feedforward signaling mediated by miR-146a deficiency. p62 is necessary for TRAF6-mediated NF-κB signaling, as disrupting the p62-TRAF6 signaling complex results in cell-cycle arrest and apoptosis of MDS/AML cells. Thus, del(5q) HR MDS/AML employs an intrachromosomal gene network involving loss of miR-146a and haploid overexpression of p62 via NF-κB to sustain TRAF6/NF-κB signaling for cell survival and proliferation. Interfering with the p62-TRAF6 signaling complex represents a therapeutic option in miR-146a-deficient and aggressive del(5q) MDS/AML

mTOR Kinase Inhibition Effectively Decreases Progression of a Subset of Neuroendocrine Tumors that Progress on Rapalog Therapy and Delays Cardiac Impairment

Inhibition of mTOR signaling using the rapalog everolimus is an FDA-approved targeted therapy for patients with lung and gastroenteropancreatic neuroendocrine tumors (NET). However, patients eventually progress on treatment, highlighting the need for additional therapies. We focused on pancreatic NETs (pNET) and reasoned that treatment of these tumors upon progression on rapalog therapy, with an mTOR kinase inhibitor (mTORKi), such as CC-223, could overcome a number of resistance mechanisms in tumors and delay cardiac carcinoid disease. We performed preclinical studies using human pNET cells in vitro and injected them subcutaneously or orthotopically to determine tumor progression and cardiac function in mice treated with either rapamycin alone or switched to CC-223 upon progression. Detailed signaling and RNA sequencing analyses were performed on tumors that were sensitive or progressed on mTOR treatment. Approximately 57% of mice bearing pNET tumors that progressed on rapalog therapy showed a significant decrease in tumor volume upon a switch to CC-223. Moreover, mice treated with an mTORKi exhibited decreased cardiac dilation and thickening of heart valves than those treated with placebo or rapamycin alone. In conclusion, in the majority of pNETs that progress on rapalogs, it is possible to reduce disease progression using an mTORKi, such as CC-223. Moreover, CC-223 had an additional transient cardiac benefit on valvular fibrosis compared with placebo- or rapalog-treated mice. These results provide the preclinical rationale to further develop mTORKi clinically upon progression on rapalog therapy and to further test their long-term cardioprotective benefit in those NET patients prone to carcinoid syndrome

Defects in the Fanconi Anemia Pathway in Head and Neck Cancer Cells Stimulate Tumor Cell Invasion through DNA-PK and Rac1 Signaling

PURPOSE: Head and neck squamous cell carcinoma (HNSCC) remains a devastating disease, and Fanconi anemia (FA) gene mutations and transcriptional repression are common. Invasive tumor behavior is associated with poor outcome, but relevant pathways triggering invasion are poorly understood. There is a significant need to improve our understanding of genetic pathways and molecular mechanisms driving advanced tumor phenotypes, to develop tailored therapies. Here we sought to investigate the phenotypic and molecular consequences of FA pathway loss in HNSCC cells. EXPERIMENTAL DESIGN: Using sporadic HNSCC cell lines with and without FA gene knockdown, we sought to characterize the phenotypic and molecular consequences of FA deficiency. FA pathway inactivation was confirmed by the detection of classic hallmarks of FA following exposure to DNA cross-linkers. Cells were subjected to RNA sequencing with qRT-PCR validation, followed by cellular adhesion and invasion assays in the presence and absence of DNA-dependent protein kinase (DNA-PK) and Rac1 inhibitors. RESULTS: We demonstrate that FA loss in HNSCC cells leads to cytoskeletal reorganization and invasive tumor cell behavior in the absence of proliferative gains. We further demonstrate that cellular invasion following FA loss is mediated, at least in part, through NHEJ-associated DNA-PK and downstream Rac1 GTPase activity. CONCLUSIONS: These findings demonstrate that FA loss stimulates HNSCC cell motility and invasion, and implicate a targetable DNA-PK/Rac1 signaling axis in advanced tumor phenotypes

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

Evolutionary rate and expression noise of the static and dynamic modules

<p><b>Copyright information:</b></p><p>Taken from "Revealing static and dynamic modular architecture of the eukaryotic protein interaction network"</p><p></p><p>Molecular Systems Biology 2007;3():110-110.</p><p>Published online 24 Apr 2007</p><p>PMCID:PMC1865589.</p><p>Copyright © 2007, EMBO and Nature Publishing Group</p> Evolutionary rates of yeast proteins derived by were used. () Boxplot of evolutionary rates of family, party and date hubs. Family hubs are static hubs with neighborhood EVs of 0.45 and date hubs are those with neighborhood EV>0.3 and avPC

NetWalker: a contextual network analysis tool for functional genomics

BACKGROUND: Functional analyses of genomic data within the context of a priori biomolecular networks can give valuable mechanistic insights. However, such analyses are not a trivial task, owing to the complexity of biological networks and lack of computational methods for their effective integration with experimental data. RESULTS: We developed a software application suite, NetWalker, as a one-stop platform featuring a number of novel holistic (i.e. assesses the whole data distribution without requiring data cutoffs) data integration and analysis methods for network-based comparative interpretations of genome-scale data. The central analysis components, NetWalk and FunWalk, are novel random walk-based network analysis methods that provide unique analysis capabilities to assess the entire data distributions together with network connectivity to prioritize molecular and functional networks, respectively, most highlighted in the supplied data. Extensive inter-operability between the analysis components and with external applications, including R, adds to the flexibility of data analyses. Here, we present a detailed computational analysis of our microarray gene expression data from MCF7 cells treated with lethal and sublethal doses of doxorubicin. CONCLUSION: NetWalker, a detailed step-by-step tutorial containing the analyses presented in this paper and a manual are available at the web site http://netwalkersuite.org