Original article | published - printed | peer reviewed
Characterization of the EGFR interactome reveals associated protein complex networks and intracellular receptor dynamics
PROTEOMICS
2013 ;
13(21):
3131 - 3144
Bibliometric indicators
Impact Factor = 3.973
Citations (WOS) = 44
DOI = 10.1002/pmic.201300154
PubMed-ID = 23956138
Authors
Foerster S*, Kacprowski T1, Dhople V1, Hammer E1, Herzog S2, Saafan H3, Bien-Möller S2, Albrecht M, Völker U1, Ritter C3
Affiliations
Abstract
Growth factor receptor mediated signaling is meanwhile recognized as a complex signaling network, which is initiated by recruiting specific patterns of adaptor proteins to the intracellular domain of epidermal growth factor receptor (EGFR). Approaches to globally identify EGFR-binding proteins are required to elucidate this network. We affinity-purified EGFR with its interacting proteins by coprecipitation from lysates of A431 cells. A total of 183 proteins were repeatedly detected in high-resolution MS measurements. For 15 of these, direct interactions with EGFR were listed in the iRefIndex interaction database, including Grb2, shc-1, SOS1 and 2, STAT 1 and 3, AP2, UBS3B, and ERRFI. The newly developed Cytoscape plugin ModuleGraph allowed retrieving and visualizing 93 well-described protein complexes that contained at least one of the proteins found to interact with EGFR in our experiments. Abundances of 14 proteins were modulated more than twofold upon EGFR activation whereof clathrin-associated adaptor complex AP-2 showed 4.6-fold enrichment. These proteins were further annotated with different cellular compartments. Finally, interactions of AP-2 proteins and the newly discovered interaction of CIP2A could be verified. In conclusion, a powerful technique is presented that allowed identification and quantitative assessment of the EGFR interactome to provide further insight into EGFR signaling.
Published in
PROTEOMICS
| Year | 2013 |
| Impact Factor (2013) | 3.973 |
| Volume | 13 |
| Issue | 21 |
| Pages | 3131 - 3144 |
| Open Access | nein |
| Peer reviewed | ja |
| Article type | Original article |
| Article state | published - printed |
| DOI | 10.1002/pmic.201300154 |
| PubMed-ID | 23956138 |
Common journal data
Short name: PROTEOMICS
ISSN: 1615-9853
eISSN: 1615-9861
Country: GERMANY (FED REP GER)
Language: English
Categories:
Impact factor trend
ISSN: 1615-9853
eISSN: 1615-9861
Country: GERMANY (FED REP GER)
Language: English
Categories:
- BIOCHEMISTRY & MOLECULAR BIOLOGY
- BIOCHEMICAL RESEARCH METHODS
Impact factor trend
| Year | Impact Factor |
|---|---|
| 2008 | 4.586 |
| 2009 | 4.426 |
| 2010 | 4.815 |
| 2011 | 4.505 |
| 2012 | 4.132 |
| 2013 | 3.973 |
| 2014 | 3.807 |
| 2015 | 4.079 |
| 2016 | 4.041 |
| 2017 | 3.532 |
| 2018 | 3.106 |
| 2019 | 3.254 |
| 2020 | 3.984 |
| 2021 | 5.393 |
| 2022 | 3.4 |
| 2023 | 3.4 |
| 2024 | 3.9 |
Projects
GANI_MED Teilprojekt PB1 1: Personalized Proteomics
GANI_MED Teilprojekt SB2: Bioinformatik
GANI_MED Greifswald Approach to Individualized Medicine (Projektverbund)
GANI_MED Teilprojekt SB2: Bioinformatik
GANI_MED Greifswald Approach to Individualized Medicine (Projektverbund)

