Physical Interaction Networks Research Articles

Canonical and single-cell Hi-C reveal distinct chromatin interaction sub-networks of mammalian transcription factors

BackgroundTranscription factor (TF) binding to regulatory DNA sites is a key determinant of cell identity within multi-cellular organisms and has been studied extensively in relation to site affinity and chromatin modifications. There has been a strong focus on the inference of TF-gene regulatory networks and TF-TF physical interaction networks. Here, we present a third type of TF network, the spatial network of co-localized TF binding sites within the three-dimensional genome.ResultsUsing published canonical Hi-C data and single-cell genome structures, we assess the spatial proximity of a genome-wide array of potential TF-TF co-localizations in human and mouse cell lines. For individual TFs, the abundance of occupied binding sites shows a positive correspondence with their clustering in three dimensions, and this is especially apparent for weak TF binding sites and at enhancer regions. An analysis between different TF proteins identifies significantly proximal pairs, which are enriched in reported physical interactions. Furthermore, clustering of different TFs based on proximity enrichment identifies two partially segregated co-localization sub-networks, involving different TFs in different cell types. Using data from both human lymphoblastoid cells and mouse embryonic stem cells, we find that these sub-networks are enriched within, but not exclusive to, different chromosome sub-compartments that have been identified previously in Hi-C data.ConclusionsThis suggests that the association of TFs within spatial networks is closely coupled to gene regulatory networks. This applies to both differentiated and undifferentiated cells and is a potential causal link between lineage-specific TF binding and chromosome sub-compartment segregation.

Tuning the structure and mechanical properties of double-network elastomer: Molecular dynamics simulation

Double-network polymers constructed by physical interaction network and covalent network, have aroused many concerns for their improved mechanical properties. Even though many experiments have revealed how to combine the physical interaction network with the covalent network, we still need a understanding of the structure-property relationship in the double-network polymers. Through coarse-grained molecular dynamics (MD) simulation, we have successfully designed the double-network system consisting of repairable physical interaction network and covalent cross-linking network. Firstly, we examined the kinetic of the covalent cross-linking reaction under high temperature ( T *>2.0 k B T / e ), which is consistence with the experiment. With the model above, we try to tune the mechanical properties by adjusting the covalent cross-linking density and the strength of non-bonding physical interaction. By systematically analyzing the radial distribution function, the virial coefficient of the beads constructing the physical interaction network under equilibration state and the stress-strain curves of all systems under unixial deformation, we found that the strength of non- bonding interaction finally dominate the contribution from physical network on the mechanical properties. Typically, only when the strength of non-bonding interaction of the physical network is higher than 5.0 e can the physical network finally form via the aggregation of physical cross-linking sites and then enhance the mechanical properties of the system. Meanwhile, the covalent network with high cross-linking density evidently perform an improvement of the tensile stress, which also better promote the contribution from physical network on the total non-bonding interaction energy. Moreover, the stress relaxation curves of double-network systems with different covalent cross-linking density also validate that the coarse-grained cross-linking process is reasonable. Finally, to investigate the self-healing property of the physical interaction network, we defined the efficiency of self-healing and study the effect of temperature. The physical interaction network could be fully reversible at high temperature ( T *>1.0 k B T / e ), though it cannot fully recover to its initial state after long enough self-healing process at the normal temperature T *>1.0 k B T / e . In general, we found the threshold of the physical interaction network where the physical network can effectively strength the mechanical properties. Constructing double-network with proper strength of physical interaction (above 12.5 kJ mol-1) may achieve the formation and good dispersion of physical cross-linking sites. Recently, the double-network polymer consisting of the physical and chemical cross-linking network has become the new trend, which may combine good mechanical properties and self-healing properties. Our work above may provide some guidances for the design of high-performance elastomer by studying the structure double-network, and it also meets the demand of tuning the structure and property of the double-network polymer.

A type 2 diabetes disease module with a high collective influence for Cdk2 and PTPLAD1 is localized in endosomes.

Despite the identification of many susceptibility genes our knowledge of the underlying mechanisms responsible for complex disease remains limited. Here, we identified a type 2 diabetes disease module in endosomes, and validate it for functional relevance on selected nodes. Using hepatic Golgi/endosomes fractions, we established a proteome of insulin receptor-containing endosomes that allowed the study of physical protein interaction networks on a type 2 diabetes background. The resulting collated network is formed by 313 nodes and 1147 edges with a topology organized around a few major hubs with Cdk2 displaying the highest collective influence. Overall, 88% of the nodes are associated with the type 2 diabetes genetic risk, including 101 new candidates. The Type 2 diabetes module is enriched with cytoskeleton and luminal acidification–dependent processes that are shared with secretion-related mechanisms. We identified new signaling pathways driven by Cdk2 and PTPLAD1 whose expression affects the association of the insulin receptor with TUBA, TUBB, the actin component ACTB and the endosomal sorting markers Rab5c and Rab11a. Therefore, the interactome of internalized insulin receptors reveals the presence of a type 2 diabetes disease module enriched in new layers of feedback loops required for insulin signaling, clearance and islet biology.

Identification of lung cancer specific differentially methylated regions using genome-wide DNA methylation study

Many molecular biomarkers have been suggested for the diagnosis, prognosis, and treatment response of lung cancer, but their clinical availability remains limited. DNA methylation is one such promising biomarker because it is stable and easily detected. We conducted an epigenome-wide analysis using methyl binding domain (MBD) sequencing in tissues of lung cancer patients. Tumor and normal tissues were obtained from two patients who underwent surgery for non-small cell lung cancer. HOXA5, HOXA9, and other related genes were associated with the CGI-located DMRs in Patient 1, whereas the SSTR5 gene was found to be associated with the CGI-located DMRs in Patient 2. Interestingly, these DMR genes were mapped in the physical interaction networks that included previously known nonsmall cell lung cancer genes. This genome-wide DNA methylation study showed an association between newly identified DMRs in CpG island promoter regions and previously known target genes for lung cancer.

The cell wall hydrolase Pmp23 is important for assembly and stability of the division ring in Streptococcus pneumoniae

Bacterial division is intimately linked to synthesis and remodeling of the peptidoglycan, a cage-like polymer that surrounds the bacterial cell, providing shape and mechanical resistance. The bacterial division machinery, which is scaffolded by the cytoskeleton protein FtsZ, includes proteins with enzymatic, structural or regulatory functions. These proteins establish a complex network of transient functional and/or physical interactions which preserve cell shape and cell integrity. Cell wall hydrolases required for peptidoglycan remodeling are major contributors to this mechanism. Consistent with this, their deletion or depletion often results in morphological and/or division defects. However, the exact function of most of them remains elusive. In this work, we show that the putative lysozyme activity of the cell wall hydrolase Pmp23 is important for proper morphology and cell division in the opportunistic human pathogen Streptococcus pneumoniae. Our data indicate that active Pmp23 is required for proper localization of the Z-ring and the FtsZ-positioning protein MapZ. In addition, Pmp23 localizes to the division site and interacts directly with the essential peptidoglycan synthase PBP2x. Altogether, our data reveal a new regulatory function for peptidoglycan hydrolases.

Towards a Numerical Approach of Finding Candidates for Additive Manufacturing-Enabled Part Consolidation

Part consolidation (PC) is one of the typical design freedoms enabled by additive manufacturing (AM) processes. However, how to select potential candidates for PC is rarely discussed. This deficiency has hindered AM from wider applications in industry. Currently available design guidelines are based on obsolete heuristic rules provided for conventional manufacturing processes. This paper first revises these rules to take account of AM constraints and lifecycle factors so that efforts can be saved and used at the downstream detailed design stage. To automate the implementation of these revised rules, a numerical approach named PC candidate detection (PCCD) framework is proposed. This framework is comprised of two steps: construct functional and physical interaction (FPI) network and PCCD algorithm. FPI network is to abstractly represent the interaction relations between components as a graph whose nodes and edges have defined physical attributes. These attributes are taken as inputs for the PCCD algorithm to verify conformance to the revised rules. In this PCCD algorithm, verification sequence of rules, conflict handling, and the optimum grouping approach with the minimum part count are studied. Compared to manual ad hoc design practices, the proposed PCCD method shows promise in repeatability, retrievability, and efficiency. Two case studies of a throttle pedal and a tripod are presented to show the application and effectiveness of the proposed methods.

A systematic atlas of chaperome deregulation topologies across the human cancer landscape.

Proteome balance is safeguarded by the proteostasis network (PN), an intricately regulated network of conserved processes that evolved to maintain native function of the diverse ensemble of protein species, ensuring cellular and organismal health. Proteostasis imbalances and collapse are implicated in a spectrum of human diseases, from neurodegeneration to cancer. The characteristics of PN disease alterations however have not been assessed in a systematic way. Since the chaperome is among the central components of the PN, we focused on the chaperome in our study by utilizing a curated functional ontology of the human chaperome that we connect in a high-confidence physical protein-protein interaction network. Challenged by the lack of a systems-level understanding of proteostasis alterations in the heterogeneous spectrum of human cancers, we assessed gene expression across more than 10,000 patient biopsies covering 22 solid cancers. We derived a novel customized Meta-PCA dimension reduction approach yielding M-scores as quantitative indicators of disease expression changes to condense the complexity of cancer transcriptomics datasets into quantitative functional network topographies. We confirm upregulation of the HSP90 family and also highlight HSP60s, Prefoldins, HSP100s, ER- and mitochondria-specific chaperones as pan-cancer enriched. Our analysis also reveals a surprisingly consistent strong downregulation of small heat shock proteins (sHSPs) and we stratify two cancer groups based on the preferential upregulation of ATP-dependent chaperones. Strikingly, our analyses highlight similarities between stem cell and cancer proteostasis, and diametrically opposed chaperome deregulation between cancers and neurodegenerative diseases. We developed a web-based Proteostasis Profiler tool (Pro2) enabling intuitive analysis and visual exploration of proteostasis disease alterations using gene expression data. Our study showcases a comprehensive profiling of chaperome shifts in human cancers and sets the stage for a systematic global analysis of PN alterations across the human diseasome towards novel hypotheses for therapeutic network re-adjustment in proteostasis disorders.

An Atlas of Protein-Protein Interactions Across Mammalian Tissues

Cellular processes arise from the dynamic organization of proteins in networks of physical interactions. Mapping the complete network of biologically relevant protein-protein interactions, the interactome, has therefore been a central objective of high-throughput biology. Yet, because widely used methods for high-throughput interaction discovery rely on heterologous expression or genetically manipulated cell lines, the dynamics of protein interactions across physiological contexts are poorly understood. Here, we use a quantitative proteomic approach combining protein correlation profiling with stable isotope labelling of mammals (PCP–SILAM) to map the interactomes of seven mouse tissues. The resulting maps provide the first proteome-scale survey of interactome dynamics across mammalian tissues, revealing over 27,000 unique interactions with an accuracy comparable to the highest-quality human screens. We identify systematic suppression of cross-talk between the evolutionarily ancient housekeeping interactome and younger, tissue-specific modules. Rewiring of protein interactions across tissues is widespread, and is poorly predicted by gene expression or coexpression. Rewired proteins are tightly regulated by multiple cellular mechanisms and implicated in disease. Our study opens up new avenues to uncover regulatory mechanisms that shape in vivo interactome responses to physiological and pathophysiological stimuli in mammalian systems.

Genes uniquely expressed in human growth plate chondrocytes uncover a distinct regulatory network

BackgroundChondrogenesis is the earliest stage of skeletal development and is a highly dynamic process, integrating the activities and functions of transcription factors, cell signaling molecules and extracellular matrix proteins. The molecular mechanisms underlying chondrogenesis have been extensively studied and multiple key regulators of this process have been identified. However, a genome-wide overview of the gene regulatory network in chondrogenesis has not been achieved.ResultsIn this study, employing RNA sequencing, we identified 332 protein coding genes and 34 long non-coding RNA (lncRNA) genes that are highly selectively expressed in human fetal growth plate chondrocytes. Among the protein coding genes, 32 genes were associated with 62 distinct human skeletal disorders and 153 genes were associated with skeletal defects in knockout mice, confirming their essential roles in skeletal formation. These gene products formed a comprehensive physical interaction network and participated in multiple cellular processes regulating skeletal development. The data also revealed 34 transcription factors and 11,334 distal enhancers that were uniquely active in chondrocytes, functioning as transcriptional regulators for the cartilage-selective genes.ConclusionsOur findings revealed a complex gene regulatory network controlling skeletal development whereby transcription factors, enhancers and lncRNAs participate in chondrogenesis by transcriptional regulation of key genes. Additionally, the cartilage-selective genes represent candidate genes for unsolved human skeletal disorders.

Global landscape of cell envelope protein complexes in Escherichia coli.

Bacterial cell envelope protein (CEP) complexes mediate a range of processes, including membrane assembly, antibiotic resistance and metabolic coordination. However, only limited characterization of relevant macromolecules has been reported to date. Here we present a proteomic survey of 1,347 CEPs encompassing 90% inner- and outer-membrane and periplasmic proteins of Escherichia coli. After extraction with non-denaturing detergents, we affinity-purified 785 endogenously tagged CEPs and identified stably associated polypeptides by precision mass spectrometry. The resulting high-quality physical interaction network, comprising 77% of targeted CEPs, revealed many previously uncharacterized heteromeric complexes. We found that the secretion of autotransporters requires translocation and the assembly module TamB to nucleate proper folding from periplasm to cell surface through a cooperative mechanism involving the β-barrel assembly machinery. We also establish that an ABC transporter of unknown function, YadH, together with the Mla system preserves outer membrane lipid asymmetry. This E. coli CEP 'interactome' provides insights into the functional landscape governing CE systems essential to bacterial growth, metabolism and drug resistance.

Transition to reconstructibility in weakly coupled networks.

Across scientific disciplines, thresholded pairwise measures of statistical dependence between time series are taken as proxies for the interactions between the dynamical units of a network. Yet such correlation measures often fail to reflect the underlying physical interactions accurately. Here we systematically study the problem of reconstructing direct physical interaction networks from thresholding correlations. We explicate how local common cause and relay structures, heterogeneous in-degrees and non-local structural properties of the network generally hinder reconstructibility. However, in the limit of weak coupling strengths we prove that stationary systems with dynamics close to a given operating point transition to universal reconstructiblity across all network topologies.

Identification of 19 new risk loci and potential regulatory mechanisms influencing susceptibility to testicular germ cell tumor.

Genome-wide association studies (GWAS) have transformed understanding of susceptibility to testicular germ cell tumors (TGCTs), but much of the heritability remains unexplained. Here we report a new GWAS, a meta-analysis with previous GWAS and a replication series, totaling 7,319 TGCT cases and 23,082 controls. We identify 19 new TGCT risk loci, roughly doubling the number of known TGCT risk loci to 44. By performing in situ Hi-C in TGCT cells, we provide evidence for a network of physical interactions among all 44 TGCT risk SNPs and candidate causal genes. Our findings implicate widespread disruption of developmental transcriptional regulators as a basis of TGCT susceptibility, consistent with failed primordial germ cell differentiation as an initiating step in oncogenesis. Defective microtubule assembly and dysregulation of KIT-MAPK signaling also feature as recurrently disrupted pathways. Our findings support a polygenic model of risk and provide insight into the biological basis of TGCT.

Revealing physical interaction networks from statistics of collective dynamics.

Revealing physical interactions in complex systems from observed collective dynamics constitutes a fundamental inverse problem in science. Current reconstruction methods require access to a system's model or dynamical data at a level of detail often not available. We exploit changes in invariant measures, in particular distributions of sampled states of the system in response to driving signals, and use compressed sensing to reveal physical interaction networks. Dynamical observations following driving suffice to infer physical connectivity even if they are temporally disordered, are acquired at large sampling intervals, and stem from different experiments. Testing various nonlinear dynamic processes emerging on artificial and real network topologies indicates high reconstruction quality for existence as well as type of interactions. These results advance our ability to reveal physical interaction networks in complex synthetic and natural systems.

Functional Metabolomics Describes the Yeast Biosynthetic Regulome

SummaryGenome-metabolism interactions enable cell growth. To probe the extent of these interactions and delineate their functional contributions, we quantified the Saccharomyces amino acid metabolome and its response to systematic gene deletion. Over one-third of coding genes, in particular those important for chromatin dynamics, translation, and transport, contribute to biosynthetic metabolism. Specific amino acid signatures characterize genes of similar function. This enabled us to exploit functional metabolomics to connect metabolic regulators to their effectors, as exemplified by TORC1, whose inhibition in exponentially growing cells is shown to match an interruption in endomembrane transport. Providing orthogonal information compared to physical and genetic interaction networks, metabolomic signatures cluster more than half of the so far uncharacterized yeast genes and provide functional annotation for them. A major part of coding genes is therefore participating in gene-metabolism interactions that expose the metabolism regulatory network and enable access to an underexplored space in gene function.

A genome-wide imaging-based screening to identify genes involved in synphilin-1 inclusion formation in Saccharomyces cerevisiae.

Synphilin-1 is a major component of Parkinson’s disease (PD) inclusion bodies implicated in PD pathogenesis. However, the machinery controlling synphilin-1 inclusion formation remains unclear. Here, we investigated synphilin-1 inclusion formation using a systematic genome-wide, high-content imaging based screening approach (HCI) in the yeast Saccharomyces cerevisiae. By combining with a secondary screening for mutants showing significant changes on fluorescence signal intensity, we filtered out hits that significantly decreased the expression level of synphilin-1. We found 133 yeast genes that didn’t affect synphilin-1 expression but that were required for the formation of synphilin-1 inclusions. Functional enrichment and physical interaction network analysis revealed these genes to encode for functions involved in cytoskeleton organization, histone modification, sister chromatid segregation, glycolipid biosynthetic process, DNA repair and replication. All hits were confirmed by conventional microscopy. Complementation assays were performed with a selected group of mutants, results indicated that the observed phenotypic changes in synphilin-1 inclusion formation were directly caused by the loss of corresponding genes of the deletion mutants. Further growth assays of these mutants showed a significant synthetic sick effect upon synphilin-1 expression, which supports the hypothesis that matured inclusions represent an end stage of several events meant to protect cells against the synphilin-1 cytotoxicity.

Abstract 4607: Mediator kinase module as a transducer of oncogenic Wnt/beta-catenin signaling

Abstract Dysregulation of Wnt/β-catenin signaling promotes colorectal cancer (CRC) and other types of malignancies through unprogrammed changes in gene transcription that drive tumorigenesis. CDK8 is a CRC oncoprotein whose amplification-dependent overexpression identifies a significant subset of CRC patients with poor prognosis. CDK8 kinase activity, along with Cyclin C (CycC), drives tumorigenesis by stimulating β-catenin transcriptional activity. Thus, inhibition of CDK8 kinase function offers a promising therapeutic approach for CDK8-overexpressing CRCs. CycC-CDK8, along with MED12 and MED13, compose a four-subunit “kinase” module within Mediator, a conserved multiprotein interface between gene-specific transcription factors and RNA Polymerase II. We have previously identified a network of physical and functional interactions within the Mediator kinase module critical for oncogenic Wnt/β-catenin signaling. Mechanistically, β-catenin binds directly to MED12 in Mediator, thus activating CycC-dependent CDK8 kinase activity and β-catenin transcriptional activity. More specifically, MED12-dependent CDK8 activation occurs through a direct interaction involving the MED12 N-terminus and a phylogenetically conserved surface groove on CycC. Here, we demonstrate that mutagenic disruption of the MED12/CycC interface in CRC cell lines leads to uncoupling of CycC-CDK8 to MED12 and core Mediator, and concomitant loss of Mediator-associated CDK8 activity. Transcriptome analysis of cells lacking CDK8 kinase function revealed downregulation of Wnt/β-catenin signaling and other oncogenic pathways, consequently impairing CRC cell proliferation and clonogenicity. Our studies therefore identify the MED12/CycC interface as a critical transducer of oncogenic Wnt/β-catenin signaling. By validating MED12/CycC as a potential therapeutic target, our findings have significant implications in current methods of treating CRC as they pave a way for development of novel, targeted inhibitors of Wnt/β-catenin signaling to treat colorectal and other CDK8-driven driven malignancies. Supported by MH085320-05 (TGB), RP140435 (TGB) and T32DE14318 (ADC). Citation Format: Alison Clark, Marieke Oldenbroek, Yao Wang, Jason Spaeth, Fangjian Gao, Victor X. Jin, Thomas Boyer. Mediator kinase module as a transducer of oncogenic Wnt/beta-catenin signaling. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4607.

Establishing a transcription factors protein network for lymphangiogenesis: identification of binding domains, impact of pathological mutations and perturbations by small molecules

As a major route of cancer metastasis is via the lymphatic vasculature, therapeutic manipulation of neo‐lymphangiogenesis is an important adjunct treatment for cancer. So far only anti‐VEGF signalling molecules have been developed to counteract tumour‐induced de novo vessel formation, yet transcription factors could provide a wealth of potential molecular targets.Classic reverse genetic approaches in mice have validated individual roles for Sox18, Prox1, Nr2f2 (COUP‐TFII) and Gata2 in lymphangiogenesis. Expression of SOX18, COUP‐TFII and GATA2 coincide only in discrete locations within the vasculature and result in the induction of lymphatic endothelial progenitor cell specification. However, no physical interaction network between those key players has been identified yet.Here we map the protein‐protein interactions between transcription factors important for lymphatic vascular development to identify key transcription factor interactions as novel potential therapeutic targets. The direct interactions between SOX18, COUP‐TFII, GATA2, MEF2C and components of the Notch pathway (RBPJ, HEY1) have been assessed in vitro using a proximity assay (AlphaScreen) coupled to cell‐free expression of the proteins. These data allowed to reconstitute the network of interactions involved in the control of lymphatic endothelial cell specification.Using truncations, we established a comprehensive molecular mapping of the dimerization and protein binding domains of SOX18. Further, we defined how those interactions are affected by mutations associated with lymphatic diseases, such as Hypotrichosis‐Lymphedema‐Telangiectasia.We then used this network as a tool for drug discovery. Perturbations created by small molecules that impact lymphangiogenesis in vivo were measured on the whole network. Inhibition of direct interactions was measured and long‐scale modifications of the network could be inferred. This molecular dissection then provides new information to understand how the physiological effects of drugs are mediated.

A combined binary interaction and phenotypic map of C.elegans cell polarity proteins.

The establishment of cell polarity is an essential process for the development of multicellular organisms and the functioning of cells and tissues. Here, we combine large-scale protein interaction mapping with systematic phenotypic profiling to study the network of physical interactions that underlies polarity establishment and maintenance in the nematode Caenorhabditis elegans. Using a fragment-based yeast two-hybrid strategy, we identified 439 interactions between 296 proteins, as well as the protein regions that mediate these interactions. Phenotypic profiling of the network resulted in the identification of 100 physically interacting protein pairs for which RNAi-mediated depletion caused a defect in the same polarity-related process. We demonstrate the predictive capabilities of the network by showing that the physical interaction between the RhoGAP PAC-1 and PAR-6 is required for radial polarization of the C. elegans embryo. Our network represents a valuable resource of candidate interactions that can be used to further our insight into cell polarization.

Detecting Protein Complexes from Signed Protein-Protein Interaction Networks.

Identification of protein complexes is fundamental for understanding the cellular functional organization. With the accumulation of physical protein-protein interaction (PPI) data, computational detection of protein complexes from available PPI networks has drawn a lot of attentions. While most of the existing protein complex detection algorithms focus on analyzing the physical protein-protein interaction network, none of them take into account the "signs" (i.e., activation-inhibition relationships) of physical interactions. As the "signs" of interactions reflect the way proteins communicate, considering the "signs" of interactions can not only increase the accuracy of protein complex identification, but also deepen our understanding of the mechanisms of cell functions. In this study, we proposed a novel Signed Graph regularized Nonnegative Matrix Factorization (SGNMF) model to identify protein complexes from signed PPI networks. In our experiments, we compared the results collected by our model on signed PPI networks with those predicted by the state-of-the-art complex detection techniques on the original unsigned PPI networks. We observed that considering the "signs" of interactions significantly benefits the detection of protein complexes. Furthermore, based on the predicted complexes, we predicted a set of signed complex-complex interactions for each dataset, which provides a novel insight of the higher level organization of the cell. All the experimental results and codes can be downloaded from http://mail.sysu.edu.cn/home/stsddq@mail.sysu.edu.cn/dai/others/SGNMF.zip.

Detecting modules in biological networks by edge weight clustering and entropy significance.

Detection of the modular structure of biological networks is of interest to researchers adopting a systems perspective for the analysis of omics data. Computational systems biology has provided a rich array of methods for network clustering. To date, the majority of approaches address this task through a network node classification based on topological or external quantifiable properties of network nodes. Conversely, numerical properties of network edges are underused, even though the information content which can be associated with network edges has augmented due to steady advances in molecular biology technology over the last decade. Properly accounting for network edges in the development of clustering approaches can become crucial to improve quantitative interpretation of omics data, finally resulting in more biologically plausible models. In this study, we present a novel technique for network module detection, named WG-Cluster (Weighted Graph CLUSTERing). WG-Cluster's notable features, compared to current approaches, lie in: (1) the simultaneous exploitation of network node and edge weights to improve the biological interpretability of the connected components detected, (2) the assessment of their statistical significance, and (3) the identification of emerging topological properties in the detected connected components. WG-Cluster utilizes three major steps: (i) an unsupervised version of k-means edge-based algorithm detects sub-graphs with similar edge weights, (ii) a fast-greedy algorithm detects connected components which are then scored and selected according to the statistical significance of their scores, and (iii) an analysis of the convolution between sub-graph mean edge weight and connected component score provides a summarizing view of the connected components. WG-Cluster can be applied to directed and undirected networks of different types of interacting entities and scales up to large omics data sets. Here, we show that WG-Cluster can be successfully used in the differential analysis of physical protein–protein interaction (PPI) networks. Specifically, applying WG-Cluster to a PPI network weighted by measurements of differential gene expression permits to explore the changes in network topology under two distinct (normal vs. tumor) conditions. WG-Cluster code is available at https://sites.google.com/site/paolaleccapersonalpage/.