An empirical framework for binary interactome mapping

Venkatesan, Kavitha; Rual, Jean François; Vázquez, Alexei; Stelzl, Ulrich; Lemmens, Irma; Hirozane-Kishikawa, Tomoko; Hao, Tong; Zenkner, Martina; X, Xin; Goh, K.-I.; Yıldırım, Muhammed A.; Simonis, Nicolas; Heinzmann, Kathrin; Gebreab, Fana; Sahalie, Julie M.; Cevik, Sebiha; Simon, Christophe; Smet, Anne Sophie de; Dann, Elizabeth; Smolyar, Alex; Vinayagam, Arunachalam; Yu, Haiyuan; Szeto, David; Borick, Heather; Dricot, Amélie; Klitgord, Niels; Murray, Ryan R.; Lin, Chenwei; Łałowski, Maciej; Timm, Jan; Rau, Kirstin; Boone, Charles; Braun, Pascal; Cusick, Michael E.; Roth, Frederick P.; Hill, David E.; Tavernier, Jan; Wanker, Erich E.; Barabási, Albert László; Vidal, Marc

doi:10.1038/nmeth.1280

Cited by 808 publications

(765 citation statements)

References 32 publications

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“…Pagerank typically operates on networks that are either mapped incompletely, such as the www 42 , or contain many false positives and negatives, such as protein interaction networks 43 , raising a fundamental question: is the ranking of a node stable relative to other nodes in the face of such considerable network perturbations?…”

Section: Resultsmentioning

confidence: 99%

Ranking stability and super-stable nodes in complex networks

2011

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Pagerank, a network-based diffusion algorithm, has emerged as the leading method to rank web content, ecological species and even scientists. Despite its wide use, it remains unknown how the structure of the network on which it operates affects its performance. Here we show that for random networks the ranking provided by pagerank is sensitive to perturbations in the network topology, making it unreliable for incomplete or noisy systems. In contrast, in scale-free networks we predict analytically the emergence of super-stable nodes whose ranking is exceptionally stable to perturbations. We calculate the dependence of the number of super-stable nodes on network characteristics and demonstrate their presence in real networks, in agreement with the analytical predictions. These results not only deepen our understanding of the interplay between network topology and dynamical processes but also have implications in all areas where ranking has a role, from science to marketing.

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Section: Resultsmentioning

confidence: 99%

Ranking stability and super-stable nodes in complex networks

2011

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“…In addition, our screens did not recapitulate two other seed-seed interactions that had been found in other studies. This low overlap between different interactome networks is a well-known effect, and it is mainly attributed to the limited sampling of the interactome space and the detection capabilities of the different techniques (Russell and Aloy 2008;Venkatesan et al 2009). In our case, this is particularly pronounced since we specifically chose our candidate genes to maximize the number of novel interactions added to the AD-related network and for which little interaction information was known (i.e., picking genes in susceptibility regions for which no direct proof of their implication in AD had been reported).…”

Section: Resultsmentioning

confidence: 99%

“…(Venkatesan et al 2009) we sought to validate some of our interactions derived from Y2H screens with complementary strategies previously employed in the identification of interactions involving AD proteins (Xia et al 1997;Hughes et al 1998). To this end, we randomly selected a subset of genes and protein interactions from our HC set, hoping that the results obtained would represent the general trends of the whole experiment ( Fig.…”

Section: Resultsmentioning

confidence: 99%

Interactome mapping suggests new mechanistic details underlying Alzheimer's disease

Soler-López¹,

Zanzoni²,

Lluís³

et al. 2010

Genome Res.

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108

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Recent advances toward the characterization of Alzheimer's disease (AD) have permitted the identification of a dozen of genetic risk factors, although many more remain undiscovered. In parallel, works in the field of network biology have shown a strong link between protein connectivity and disease. In this manuscript, we demonstrate that AD-related genes are indeed highly interconnected and, based on this observation, we set up an interaction discovery strategy to unveil novel AD causative and susceptibility genes. In total, we report 200 high-confidence protein-protein interactions between eight confirmed AD-related genes and 66 candidates. Of these, 31 are located in chromosomal regions containing susceptibility loci related to the etiology of late-onset AD, and 17 show dysregulated expression patterns in AD patients, which makes them very good candidates for further functional studies. Interestingly, we also identified four novel direct interactions among well-characterized AD causative/susceptibility genes (i.e., APP, A2M, APOE, PSEN1, and PSEN2), which support the suggested link between plaque formation and inflammatory processes and provide insights into the intracellular regulation of APP cleavage. Finally, we contextualize the discovered relationships, integrating them with all the interaction data reported in the literature, building the most complete interactome associated to AD. This general view facilitates the analyses of global properties of the network, such as its functional modularity, and triggers many hypotheses on the molecular mechanisms implicated in AD. For instance, our analyses suggest a putative role for PDCD4 as a neuronal death regulator and ECSIT as a molecular link between oxidative stress, inflammation, and mitochondrial dysfunction in AD.

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“…What is more, the mapping of interactomes provides the insights necessary for the development of therapeutic strategies designed to disrupt specific PPI linked to disease [1]. The high-throughput screening methods most often used for mapping PPI are yeast two-hybrid and co-affinity purification assays followed by mass spectrometry [2][3][4]. The information obtained from these screening methods is supplemented with information from curated interaction datasets derived from the literature [5], and from predictive computer algorithms [6].…”

Section: Introductionmentioning

confidence: 99%

Measuring Protein Interactions Using Förster Resonance Energy Transfer and Fluorescence Lifetime Imaging Microscopy

Day

2014

Microsc Microanal

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The method of fluorescence lifetime imaging microscopy (FLIM) is a quantitative approach that can be used to detect Förster Resonance Energy Transfer (FRET). The use of FLIM to measure the FRET that results from the interactions between proteins labeled with fluorescent proteins (FPs) inside living cells provides a non-invasive method for mapping interactomes. Here, the use of the phasor plot method to analyze frequency domain (FD) FLIM measurements is described, and measurements obtained from cells producing the 'FRET standard' fusion proteins are used to validate the FLIM system for FRET measurements. The FLIM FRET approach is then used to measure both homologous and heterologous protein-protein interactions (PPI) involving the CCAAT/enhancer-binding protein alpha (C/EBPα). C/EBPα is a transcription factor that controls cell differentiation, and localizes to heterochromatin where it interacts with the heterochromatin protein 1 alpha (HP1α). The FLIM-FRET method is used to quantify the homologous interactions between the FP-labeled basic leucine zipper (BZip) domain of C/EBPα. Then the heterologous interactions between the C/EBPa BZip domain and HP1a are quantified using the FRET-FLIM method. The results demonstrate that the basic region and leucine zipper (BZip) domain of C/ EBPα is sufficient for the interaction with HP1α in regions of heterochromatin. Keywordsfluorescence lifetime imaging microscopy (FLIM); Förster resonance energy transfer (FRET) microscopy; fluorescent proteins (FPs); protein-protein interactions (PPI)

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An empirical framework for binary interactome mapping

Cited by 808 publications

References 32 publications

Ranking stability and super-stable nodes in complex networks

Ranking stability and super-stable nodes in complex networks

Interactome mapping suggests new mechanistic details underlying Alzheimer's disease

Measuring Protein Interactions Using Förster Resonance Energy Transfer and Fluorescence Lifetime Imaging Microscopy

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