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

Chaperones as integrators of cellular networks: Changes of cellular integrity in stress and diseases

Cellular networks undergo rearrangements during stress and diseases. In un-stressed state the yeast protein-protein interaction network (interactome) is highly compact, and the centrally organized modules have a large overlap. During stress several original modules became more separated, and a number of novel modules also appear. A few basic functions, such as the proteasome preserve their central position. However, several functions with high energy demand, such the cell-cycle regulation loose their original centrality during stress. A number of key stress-dependent protein complexes, such as the disaggregation-specific chaperone, Hsp104, gain centrality in the stressed yeast interactome. Molecular chaperones, heat shock, or stress proteins form complex interaction networks (the chaperome) with each other and their partners. Here we show that the human chaperome recovers the segregation of protein synthesis-coupled and stress-related chaperones observed in yeast recently. Examination of yeast and human interactomes shows that (1) chaperones are inter-modular integrators of protein-protein interaction networks, which (2) often bridge hubs and (3) are favorite candidates for extensive phosphorylation. Moreover, chaperones (4) become more central in the organization of the isolated modules of the stressed yeast protein-protein interaction network, which highlights their importance in the de-coupling and re-coupling of network modules during and after stress. Chaperone-mediated evolvability of cellular networks may play a key role in cellular adaptation during stress and various polygenic and chronic diseases, such as cancer, diabetes or neurodegeneration.Comment: 13 pages, 3 figures, 1 glossar

On dynamic network entropy in cancer

The cellular phenotype is described by a complex network of molecular interactions. Elucidating network properties that distinguish disease from the healthy cellular state is therefore of critical importance for gaining systems-level insights into disease mechanisms and ultimately for developing improved therapies. By integrating gene expression data with a protein interaction network to induce a stochastic dynamics on the network, we here demonstrate that cancer cells are characterised by an increase in the dynamic network entropy, compared to cells of normal physiology. Using a fundamental relation between the macroscopic resilience of a dynamical system and the uncertainty (entropy) in the underlying microscopic processes, we argue that cancer cells will be more robust to random gene perturbations. In addition, we formally demonstrate that gene expression differences between normal and cancer tissue are anticorrelated with local dynamic entropy changes, thus providing a systemic link between gene expression changes at the nodes and their local network dynamics. In particular, we also find that genes which drive cell-proliferation in cancer cells and which often encode oncogenes are associated with reductions in the dynamic network entropy. In summary, our results support the view that the observed increased robustness of cancer cells to perturbation and therapy may be due to an increase in the dynamic network entropy that allows cells to adapt to the new cellular stresses. Conversely, genes that exhibit local flux entropy decreases in cancer may render cancer cells more susceptible to targeted intervention and may therefore represent promising drug targets.Comment: 10 pages, 3 figures, 4 tables. Submitte

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

Revisiting Date and Party Hubs: Novel Approaches to Role Assignment in Protein Interaction Networks

The idea of 'date' and 'party' hubs has been influential in the study of protein-protein interaction networks. Date hubs display low co-expression with their partners, whilst party hubs have high co-expression. It was proposed that party hubs are local coordinators whereas date hubs are global connectors. Here we show that the reported importance of date hubs to network connectivity can in fact be attributed to a tiny subset of them. Crucially, these few, extremely central, hubs do not display particularly low expression correlation, undermining the idea of a link between this quantity and hub function. The date/party distinction was originally motivated by an approximately bimodal distribution of hub co-expression; we show that this feature is not always robust to methodological changes. Additionally, topological properties of hubs do not in general correlate with co-expression. Thus, we suggest that a date/party dichotomy is not meaningful and it might be more useful to conceive of roles for protein-protein interactions rather than individual proteins. We find significant correlations between interaction centrality and the functional similarity of the interacting proteins.Comment: 27 pages, 5 main figures, 4 supplementary figure

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

Temporal Networks

A great variety of systems in nature, society and technology -- from the web of sexual contacts to the Internet, from the nervous system to power grids -- can be modeled as graphs of vertices coupled by edges. The network structure, describing how the graph is wired, helps us understand, predict and optimize the behavior of dynamical systems. In many cases, however, the edges are not continuously active. As an example, in networks of communication via email, text messages, or phone calls, edges represent sequences of instantaneous or practically instantaneous contacts. In some cases, edges are active for non-negligible periods of time: e.g., the proximity patterns of inpatients at hospitals can be represented by a graph where an edge between two individuals is on throughout the time they are at the same ward. Like network topology, the temporal structure of edge activations can affect dynamics of systems interacting through the network, from disease contagion on the network of patients to information diffusion over an e-mail network. In this review, we present the emergent field of temporal networks, and discuss methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems. In the light of traditional network theory, one can see this framework as moving the information of when things happen from the dynamical system on the network, to the network itself. Since fundamental properties, such as the transitivity of edges, do not necessarily hold in temporal networks, many of these methods need to be quite different from those for static networks