N Interactome

Skip to Main content

Interactome

Related terms:

Mass Spectrum

Protein-Protein Interaction

Proteomics

Protein

Phenotype

Mutation

RNA

microRNA

View all Topics

Systems Cell Biology

K. Luck, ... M. Vidal, in Encyclopedia of Cell Biology, 2016

Interactome maps and cell communication

Interactome mapping of particular functional modules or biological pathways has identified new regulators and effectors of these modules or pathways, unraveling their molecular complexity and redundancy. Pathway specific interactome mapping has been carried out for proliferative pathways often deregulated in multiple diseases: PI3K-mTOR pathway (Pilot-Storck et al., 2010), the MAPK pathway (Bandyopadhyay et al., 2010), the TGFβ pathway (Tewari et al., 2004; Barrios-Rodiles et al., 2005), and the Hippo pathway (Kwon et al., 2013). The PI3K-mTOR Y2H interactome map (Pilot-Storck et al., 2010) identified 67 new interactions between 33 components of the pathway of which several were analyzed for their role in mTOR signaling. Systematic TGFβ interactome mapping and integration of the resulting interaction network with genetic perturbation data led to the identification of nine pathway modifiers within a network of 71 interactions among 59 pathway components (Tewari et al., 2004). PPI maps centered on molecular machines have been generated for the yeast mediator complex (Guglielmi et al., 2004) and the human spliceosome (Hegele et al., 2012).

Integration of protein interactome data with phenotypic and expression data furthered understanding of the DNA damage response (Boulton et al., 2002) and of early embryogenesis in worm (Gunsalus et al., 2005). Overlaying PPI data, gene expression data, genetic interaction data, and phenotypic data predicted positive and negative regulators of signaling pathways and protein complexes in fly (Vinayagam et al., 2014).

View chapterPurchase book

Application of Systems Biology in the Research of TCM Formulae

Shikai Yan, ... Weidong Zhang, in Systems Biology and its Application in TCM Formulas Research, 2018

3.1.5.6 Interactomics

The interactome is the whole set of molecular interactions that occur within a particular cell. The term interactome was originally coined in 1999 by a group of French scientists headed by Bernard Jacq,133 and is often described in terms of biological networks. Interactomics is a discipline at the intersection of bioinformatics and biology that deals with studying both the interactions and the consequences of those interactions between and among proteins and other molecules within a cell.133 Molecular interactions can occur between molecules belonging to different biochemical families or within a given family, such as proteins, nucleic acids, lipids, and carbohydrates. Interactomes may be described as biological networks, and most commonly, interactome refers to protein-protein interaction (PPI) network and protein-DNA interaction networks (also called gene regulatory networks), or subsets thereof. Therefore, a typical interactome includes transcription factors, chromatin regulatory proteins, and their target genes. Interactomics aims to compare such networks of interactions between and within species in order to discover patterns of network preservation and/or variation. Interactomic methods are currently being used to predict the function of proteins with no known function, especially in the field of drug discovery.134

View chapterPurchase book

Advances in Biomarkers of Major Depressive Disorder

Tiao-Lai Huang, Chin-Chuen Lin, in Advances in Clinical Chemistry, 2015

4.4 Protein interactomics

Interactome could be considered a biological network wherein molecular interactions of a particular protein are functionally mapped. The study of the interactome could help reveal dysfunctional pathways and possibly accelerate biomarker and therapeutic discovery [162].

Protein interactomics often employ yeast two-hybrid screening (Y2H) [163], tandem affinity purification [164], and coimmunoprecipitation (coIP) [165].

The phosphatidylinositol 3-kinase and the mammalian target of rapamycin (PI3K–mTOR) pathway may play a central role in MDD therapeutics through immune cell activation by inflammatory cytokines [166]. In an Y2H interactome analysis, 31 components of the PI3K–mTOR pathway were evaluated with respect to MDD [167]. A total of 802 interactions within the PI3K–mTOR pathway were mapped, including 67 new interactions. More importantly, the deformed epidermal autoregulatory factor-1 (DEAF1) transcription factor was identified as an interactor and in vitro substrate of GSK3A and GSK3B. GSK3 inhibitors, such as lithium, increased DEAF1 transcriptional activity on the 5-HT1A serotonin receptor promoter. As such, DEAF1 may represent a therapeutic target of lithium.

Interactomics may be useful to integrate known datasets. A comprehensive analysis framework at the systemic level was proposed using a set of MDD candidate genes [168]. This study was based on multiple lines of evidence including association, linkage, gene expression (both human and animal studies), regulatory pathway, and literature search. The resulting pathway enrichment and crosstalk analyses revealed two unique pathway interaction modules significantly enriched with MDD genes: neurotransmission and immune system related, supporting the neuropathologic hypothesis of MDD.

View chapterPurchase book

Guide to Yeast Genetics: Functional Genomics, Proteomics, and Other Systems Analysis

Matija Dreze, ... Pascal Braun, in Methods in Enzymology, 2010

5 Conclusion

Information on interactome networks constitutes a critical element of systems biology. We have spelled out a general approach to high-quality interactome mapping in which a reliable high-throughput assay is used as a primary screening platform. Subsequently, alternative validation assays are used to demonstrate data quality in a way unprejudiced by preconceived ideas and biases about what protein interactions are supposed to look like. To produce high-quality data, appropriate controls need to be implemented at every stage of a binary interactome mapping pipeline, including thorough controls for technical artifacts and subsequent experimental determination of the quality of interactome network maps. Experimental validation of primary screening data ensures data quality unbiased by current scientific perceptions and hence of greatest utility for exploring biology.

Use of this general framework of interactome mapping, the main features of which are stringent removal of technical artifacts and experimental control of data quality, will enable production of high-quality datasets.

View chapterPurchase book

Generating Diversity and Specificity through Developmental Cell Signaling

Renée V. Hoch, Philippe Soriano, in Principles of Developmental Genetics (Second Edition), 2015

Interactome Mapping

Interactome mapping, in which signaling networks are modeled on the basis of physical protein-protein interactions, is not hindered by compensatory mechanisms that may mask roles of pathway members/modifiers in functional assays. Early interactome studies relied on genome-wide yeast two-hybrid (Y2H) assays using known signaling pathway proteins as baits. For example, Tewari and colleagues conducted a genome-wide Y2H screen for C. elegans proteins that interact physically with TGFβ pathway proteins. They generated an interactome map describing physical interactions among 59 proteins, only four of which had previously been assigned to the TGFβ signaling pathway. Novel components of this biochemically-defined interactome were validated in vivo based on expression in TGFβ-dependent contexts and genetic interaction with known TGFβ pathway genes. Thus several new proteins were modeled into the TGFβ signaling network including filamin, the TTX-1 homeobox protein, Swi/Snf chromatin remodeling factors, and Hsp90 (Tewari et al., 2004). Subsequent studies validated the interactome results in other systems, for instance demonstrating roles of Hsp90 and Swi/Snf factors in TGFβ signaling regulation (Wrighton et al., 2008; Xi et al., 2008). Genome-wide Y2H analyses have been reported for C. elegans and Drosophila, and protein-protein interaction data for multiple systems have been compiled into interactive public databases (Xenarios et al., 2002; Giot et al., 2003; Li et al., 2004; Formstecher et al., 2005). Comparative studies in different cell systems may illuminate context-specific mechanisms of signal transduction. Furthermore, Y2H is amenable to rapid, easy generation and analysis of mutant proteins, and so can be used to characterize protein-protein interaction domains.

Proteomics approaches employing tandem affinity purification-mass spectrometry (TAP-MS) have also been used for interactome analysis (Gavin et al., 2002). TAP-MS, like Y2H, entails generating protein fusion "baits": TAP tags are added to C- or N-termini of baits and used for sequential purification of protein complexes, which are then analyzed by MS. Compared to Y2H, TAP-MS is less sensitive for the detection of weak interactions or interaction partners expressed at low levels. However, it can be done using a greater range of physiological cell types and can also be used to identify protein-protein interactions dependent on cell signaling. In addition to the tradeoff in sensitivity, Y2H and TAP-MS differ in that Y2H identifies mostly direct protein-protein interactions, whereas TAP-MS characterizes protein complexes and therefore generates datasets including direct and indirect interactions. For these reasons, Y2H and TAP-MS can generate complementary datasets in interactome analyses.

Whereas cell stimulation/response assays have long been used to study responses to soluble ligands, biochemical responses to contact-mediated signals (e.g., Notch/Delta, Eph/ephrin) present a technical challenge in cultured cell systems. In some cases, soluble forms of membrane-bound ligands can drive signals normally mediated by cell contact. For instance, ephrins are transmembrane and glycophosphatidylinositol (GPI)-linked ligands for the Eph RTKs, and pre-clustered ephrin-Fc molecules have been used to stimulate Eph signaling in phosphoproteomic profiling experiments (Bush and Soriano, 2010). As an alternative approach, Jørgensen et al. used stable isotope labeling (SILAC; Ong et al., 2002) to differentially label Eph- versus ephrin-expressing cells, which they then co-cultured and used in phosphoproteomic analyses of bi-directional signaling (Jorgensen et al., 2009). Phosphotyrosine proteomics data were integrated with siRNA library screening data that identified which phosphoproteins impacted cell-cell sorting. Computational algorithms then generated phosphorylation network predictions (kinase > kinase substrate > phosphotyrosine binding partner) of signaling responses in Eph- versus ephrin- expressing cells (Jorgensen et al., 2009). These experiments identified many distinct and some common signaling modules activated in EphB2- versus ephrinB1-expressing cells. In some cases, commonly activated effectors differed in the two populations as to the amplitude/dynamics of tyrosine phosphorylation responses. In addition, several kinases/effectors were found to be differentially used in forward (Eph) versus reverse (ephrin) signaling (Jorgensen et al., 2009). For instance, IGF1R and PTK2 were identified as kinases that likely mediate signal transduction by ephrinB1, which does not have intrinsic kinase activity; EphB2 and ABL1 were identified as the major kinases driving EphB2 signal transduction (Jorgensen et al., 2009). Parallel experiments with signaling-mutant forms of Eph and ephrin identified cell-autonomous effects of the mutations on downstream signaling events, and also revealed that Eph mutations impact signaling in ephrin-expressing cells and vice versa (Jorgensen et al., 2009). Future studies using these techniques may reveal cell type-specific mechanisms of Eph-ephrin signaling and could uncover distinctions between signaling by different Eph-ephrin interaction pairs.

View chapterPurchase book

Retinitis Pigmentosa and Allied Disorders

Kevin Gregory-Evans, ... Richard G. Weleber, in Retina (Fifth Edition), 2013

RPGR interactome

An interactome is a complex representation of functional interactions between molecules either within a cell or within the organism as a whole. Often such interactomes reveal important interactions between molecules that at first would not appear to be functionally related. In this way, the functional consequences of genetic mutation can be predicted without the need for lengthy experimentation. An RPGR interactome has also been proposed.502 RPGR is a component of centrioles, ciliary axonemes, and microtubular transport complexes, although its precise function is unknown. It colocalizes with RPGRIP1 at the axonemes of connecting cilia in rod and cone photoreceptors503 by binding to RPGRIP1. This localization is lost in Rpgrip1 knockout (KO) mice. Rpgr KO mice develop a slow retinal degeneration, with features resembling a cone–rod degeneration – cone photoreceptors degenerate faster than rods and there is partial mislocalization of cone opsins.504 Some residual RpgrORF15 expression has been reported in this model.505 RPGR has been shown to co-immunoprecipitate in retinal extracts with a number of different axonemal, basal body, and microtubular transport proteins.505 These include nephrocystin-5 and calmodulin, which localize to photoreceptor-connecting cilia; the microtubule-based transport proteins, kinesin II (KIF3A, KAP3 subunits), dynein (DIC subunit), SMC1, and SMC3; and two regulators of cytoplasmic dynein, p150Glued and p50-dynamitin, which tether cargoes to the dynein motor. Inhibition of dynein by overexpressing p50-dynamitin abrogates the localization of RPGRORF15 to basal bodies. RPGRORF15 can be co-immunoprecipitated from retinal extracts with other basal body proteins, including NPM, IFT88, 14–3-3ε, and γ-tubulin.502, 505 RPGRORF15 and RPGRIP1 co-localize at centrosomes in a wide variety of nonciliated cells and at basal bodies in ciliated cells. Both proteins are core components of centrioles and basal bodies.502 In summary, RPGRORF15 appears to have a role in microtubule-based transport to and from the basal bodies and within photoreceptor axonemes, perhaps concerned with movement of cargoes between IS and OS. RPGRORF15 is predominantly expressed in photoreceptor connecting cilia and basal bodies but expression has also been reported in OS in some species,506 although this has been disputed.503 RPGR is also expressed in the transitional zone of motile cilia in the epithelial lining of human bronchi and sinuses (RPGRex1–19 only) and within the human and monkey cochlea.503,507–509

View chapterPurchase book

Pan-interactomics and its applications

Gyan P. Srivastava, ... Dinesh K. Yadav, in Pan-genomics: Applications, Challenges, and Future Prospects, 2020

6 Conclusions and future perspectives

Pan-interactome mapping serve as a tool to identify the molecular networks in different physiological conditions of the cell. The prediction of unknown genes and their interactive partners can be mapped by different interactomics approaches. Modifications in sophisticated instrumentations took the interactomics research to next level. The combinations of two or more techniques provide precise understanding of pathway interactions. Different interactomics database improved the knowledge of host-parasite interaction mechanism and proved to be a milestone for better understanding of the biology of liaisons, thereby designing new approaches for more précised preventions. Moreover, deciphering the structure of infection network between the proteins and the host's cellular proteins generate new hypothesis to address the infections at molecular level to the advent of new drug discovery. The comparative interactome can also be used for exploring unknown pathway(s) or function(s) of a particular gene(s). In future it may be possible to unveil the whole cellular event of organisms by mapping interactome networks. Mutational analysis on a particular species unveils the function of all the co-expressing proteins in the absence of a single protein in any pathway. However, computational analysis needs a cross-examination through experiments or vice versa.

View chapterPurchase book

Bacteriophages, Part B

Roman Häuser, ... Peter Uetz, in Advances in Virus Research, 2012

7 The λ interactome

A λ Y2H interactome analysis has been completed (Rajagopala et al., 2011). All λ open reading frames (ORFs) were cloned and tested in all possible pairs for interactions, using three different Y2H vectors. These screens identified 97 interactions among 68 tested λ proteins, of which 16 were known previously. Notably, because only 33 interactions had been reported previously, this screen found more than 50% of all previously published interactions in a single experiment! At least 18 of the newly observed Y2H interactions seem to be plausible, based on the functions of the interacting proteins, indicating that even in λ many new discoveries can be made. Interestingly, relatively few interactions involving morphogenetic proteins were revealed, and possible reasons for the still significant fraction of apparent false negatives are discussed later.

View chapterPurchase book

Big on Bk

H. Kim, K.H. Oh, in International Review of Neurobiology, 2016

8.4.2 Interactome of Synaptic Proteins

A BK channel interactome was also built in the context of synaptic proteins. Gorini et al. carried out coimmunoprecipitation experiments in a mouse cortical membrane preparation using antibodies recognizing BK channels (Gorini et al., 2010). Coprecipitated proteins were identified by either western blot analysis or LC–MS/MS. In addition to syntaxin-1A, this study identified several proteins found in presynaptic terminals, including dynamin-1, PI-3 kinase, γ-tubulin complex components, and Na+/K+-transporting ATPase. With additional coimmunoprecipitation experiments using antibodies recognizing other presynaptic proteins, they assembled a protein–protein interaction network of BK channels, whose nodes include calcium channels, cytoskeletal proteins, and components of the synaptic vesicle fusion/recycle machinery, such as syntaxin-1A, SNAP-25 (synaptosome-associated protein of 25 kDa), VAMP-2 (vesicle-associated membrane protein-2), and dynamin-1.

View chapterPurchase book

Targeting Protein–Protein Interactions to Treat Cancer—Recent Progress and Future Directions

William Garland, ... Jaideep Chaudhary, in Annual Reports in Medicinal Chemistry, 2013

8 Conclusions

Protein complexes in the interactome provide practical drug targets for oncology drug discovery. Research on a handful of PPIs important to the growth and spread of cancer has produced agents with sufficient potency and cellular activity to become clinical candidates. The list of "tractable protein–protein targets" is growing although still small compared to the list of "considered intractable protein–protein targets." With numerous possible cancer drug targets in the interactome, thoughtful selection of PPI targets that are amenable to inhibition or stabilization by small molecules will become a critical task for researchers. However, the true test of the evolving emphasis on PPI in cancer will be the availability of PPI-based drugs to treat cancer patients.

View chapterPurchase book

Recommended publications:

Cancer Cell

Journal

FEBS Letters

Journal

Reference Module in Life Sciences

Reference work • 2016

Journal of Molecular and Cellular Cardiology

Journal

Browse Journals & Books

About ScienceDirect

Remote access

Shopping cart

Advertise

Contact and support

Terms and conditions

Privacy policy