Browsing by Subject "Protein interaction mapping"

Now showing 1 - 4 of 4
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    Mitochondria–Nucleus Shuttling FK506-Binding Protein 51 Interacts with TRAF Proteins and Facilitates the RIG-I-Like Receptor-Mediated Expression of Type I IFN
    (Public Library of Science, 2014-05-01) Akiyama, Taishin; Shiraishi, Takuma; Qin, Junwen; Konno, Hiroyasu; Akiyama, Nobuko; Shinzawa, Miho; Miyauchi, Maki; Takizawa, Nobukazu; Yanai, Hiromi; Ohashi, Hiroyuki; Miyamoto-Sato, Etsuko; Yanagawa, Hiroshi; Yong, Weidong; Shou, Weinian; Inoue, Jun-ichiro; Pediatrics, School of Medicine
    Virus-derived double-stranded RNAs (dsRNAs) are sensed in the cytosol by retinoic acid-inducible gene (RIG)-I-like receptors (RLRs). These induce the expression of type I IFN and proinflammatory cytokines through signaling pathways mediated by the mitochondrial antiviral signaling (MAVS) protein. TNF receptor-associated factor (TRAF) family proteins are reported to facilitate the RLR-dependent expression of type I IFN by interacting with MAVS. However, the precise regulatory mechanisms remain unclear. Here, we show the role of FK506-binding protein 51 (FKBP51) in regulating the dsRNA-dependent expression of type I IFN. The binding of FKBP51 to TRAF6 was first identified by "in vitro virus" selection and was subsequently confirmed with a coimmunoprecipitation assay in HEK293T cells. The TRAF-C domain of TRAF6 is required for its interaction, although FKBP51 does not contain the consensus motif for interaction with the TRAF-C domain. Besides TRAF6, we found that FKBP51 also interacts with TRAF3. The depletion of FKBP51 reduced the expression of type I IFN induced by dsRNA transfection or Newcastle disease virus infection in murine fibroblasts. Consistent with this, the FKBP51 depletion attenuated dsRNA-mediated phosphorylations of IRF3 and JNK and nuclear translocation of RelA. Interestingly, dsRNA stimulation promoted the accumulation of FKBP51 in the mitochondria. Moreover, the overexpression of FKBP51 inhibited RLR-dependent transcriptional activation, suggesting a scaffolding function for FKBP51 in the MAVS-mediated signaling pathway. Overall, we have demonstrated that FKBP51 interacts with TRAF proteins and facilitates the expression of type I IFN induced by cytosolic dsRNA. These findings suggest a novel role for FKBP51 in the innate immune response to viral infection.
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    Monitoring Protein Interactions in Living Cells with Fluorescence Lifetime Imaging Microscopy
    (Elsevier, 2012) Sun, Yuansheng; Hays, Nicole M.; Periasamy, Ammasi; Davidson, Michael W.; Day, Richard N.; Cellular and Integrative Physiology, School of Medicine
    Fluorescence lifetime imaging microscopy (FLIM) is now routinely used for dynamic measurements of signaling events inside single living cells, such as monitoring changes in intracellular ions and detecting protein-protein interactions. Here, we describe the digital frequency domain FLIM data acquisition and analysis. We describe the methods necessary to calibrate the FLIM system and demonstrate how they are used to measure the quenched donor fluorescence lifetime that results from Förster Resonance Energy Transfer (FRET). We show how the "FRET-standard" fusion proteins are used to validate the FLIM system for FRET measurements. We then show how FLIM-FRET can be used to detect the dimerization of the basic leucine zipper (B Zip) domain of the transcription factor CCAAT/enhancer binding protein α in the nuclei of living mouse pituitary cells. Importantly, the factors required for the accurate determination and reproducibility of lifetime measurements are described in detail.
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    Predicting adverse side effects of drugs
    (Springer Nature, 2011) Huang, Liang-Chin; Wu, Xiaogang; Chen, Jake Y.; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering
    Background: Studies of toxicity and unintended side effects can lead to improved drug safety and efficacy. One promising form of study comes from molecular systems biology in the form of "systems pharmacology". Systems pharmacology combines data from clinical observation and molecular biology. This approach is new, however, and there are few examples of how it can practically predict adverse reactions (ADRs) from an experimental drug with acceptable accuracy. Results: We have developed a new and practical computational framework to accurately predict ADRs of trial drugs. We combine clinical observation data with drug target data, protein-protein interaction (PPI) networks, and gene ontology (GO) annotations. We use cardiotoxicity, one of the major causes for drug withdrawals, as a case study to demonstrate the power of the framework. Our results show that an in silico model built on this framework can achieve a satisfactory cardiotoxicity ADR prediction performance (median AUC = 0.771, Accuracy = 0.675, Sensitivity = 0.632, and Specificity = 0.789). Our results also demonstrate the significance of incorporating prior knowledge, including gene networks and gene annotations, to improve future ADR assessments. Conclusions: Biomolecular network and gene annotation information can significantly improve the predictive accuracy of ADR of drugs under development. The use of PPI networks can increase prediction specificity and the use of GO annotations can increase prediction sensitivity. Using cardiotoxicity as an example, we are able to further identify cardiotoxicity-related proteins among drug target expanding PPI networks. The systems pharmacology approach that we developed in this study can be generally applicable to all future developmental drug ADR assessments and predictions.
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    STOP using just GO: a multi-ontology hypothesis generation tool for high throughput experimentation
    (Springer Nature, 2013-02-14) Wittkop, Tobias; TerAvest, Emily; Evani, Uday S.; Fleisch, K. Mathew; Berman, Ari E.; Powell, Corey; Shah, Nigam H.; Mooney, Sean D.; Medical and Molecular Genetics, School of Medicine
    Background: Gene Ontology (GO) enrichment analysis remains one of the most common methods for hypothesis generation from high throughput datasets. However, we believe that researchers strive to test other hypotheses that fall outside of GO. Here, we developed and evaluated a tool for hypothesis generation from gene or protein lists using ontological concepts present in manually curated text that describes those genes and proteins. Results: As a consequence we have developed the method Statistical Tracking of Ontological Phrases (STOP) that expands the realm of testable hypotheses in gene set enrichment analyses by integrating automated annotations of genes to terms from over 200 biomedical ontologies. While not as precise as manually curated terms, we find that the additional enriched concepts have value when coupled with traditional enrichment analyses using curated terms. Conclusion: Multiple ontologies have been developed for gene and protein annotation, by using a dataset of both manually curated GO terms and automatically recognized concepts from curated text we can expand the realm of hypotheses that can be discovered. The web application STOP is available at http://mooneygroup.org/stop/.