Protein interactions are key determinants of protein function in biological systems. Despite the potential that quantitative protein interaction information could have for all areas of cancer research, unbiased or large-scale quantitation of protein interactions within native living systems is a challenge that is unmet by today's technology. The capacity to identify and quantitate protein interactions on a large-scale within native cells, patient samples, or tissues does not currently exist. Improved capabilities to quantitate protein interactions will have a major impact on the understanding of cancer, metastasis and the development of anti-cancer drug resistance. This project aims to develop and apply quantitative cross-linking with cancer cells with advanced Protein Interaction Reporter (PIR) technology. Stable Isotope Label of Amino acids in Cell culture (SILAC) will be combined with PIR technology to allow quantitation of protein levels and protein interactions in cells. These capabilities will be applied to cisplatin-, taxol-, and SN-38 resistant cancer cells to allow quantitation of interactions relative to drug sensitive cancer cells. This project will provide the first relative quantitation data on protein interactions in cancer cells and the first unbiased measurements of functional regulation at the protein interaction level relevant to drug resistance.
Drug resistance in cancer treatment is the primary reason for therapy failure and will likely remain a primary factor leading to cancer patient death until functional regulation that supports drug resistance can be better understood. Functional regulation in all cells is achieved by changes in protein abundance, localization, interactions and topological features. This project will develop and apply advanced technology to help visualize changes in functional regulation in cancer cells that have acquired drug resistance.
|Schweppe, Devin K; Chavez, Juan D; Bruce, James E (2016) XLmap: an R package to visualize and score protein structure models based on sites of protein cross-linking. Bioinformatics 32:306-8|
|Chavez, Juan D; Schweppe, Devin K; Eng, Jimmy K et al. (2016) InÂ Vivo Conformational Dynamics of Hsp90 and Its Interactors. Cell Chem Biol 23:716-26|
|Schweppe, Devin K; Zheng, Chunxiang; Chavez, Juan D et al. (2016) XLinkDB 2.0: integrated, large-scale structural analysis of protein crosslinking data. Bioinformatics 32:2716-8|
|DeBlasio, Stacy L; Chavez, Juan D; Alexander, Mariko M et al. (2016) Visualization of Host-Polerovirus Interaction Topologies Using Protein Interaction Reporter Technology. J Virol 90:1973-87|
|Wu, Xia; Chavez, Juan D; Schweppe, Devin K et al. (2016) In vivo protein interaction network analysis reveals porin-localized antibiotic inactivation in Acinetobacter baumannii strain AB5075. Nat Commun 7:13414|
|Schweppe, Devin K; Chavez, Juan D; Navare, Arti T et al. (2016) Spectral Library Searching To Identify Cross-Linked Peptides. J Proteome Res 15:1725-31|
|Navare, Arti T; Chavez, Juan D; Zheng, Chunxiang et al. (2015) Probing the protein interaction network of Pseudomonas aeruginosa cells by chemical cross-linking mass spectrometry. Structure 23:762-73|
|Chavez, Juan D; Schweppe, Devin K; Eng, Jimmy K et al. (2015) Quantitative interactome analysis reveals a chemoresistant edgotype. Nat Commun 6:7928|
|Schweppe, Devin K; Harding, Christopher; Chavez, Juan D et al. (2015) Host-Microbe Protein Interactions during Bacterial Infection. Chem Biol 22:1521-30|
|Wu, Xia; Held, Kiara; Zheng, Chunxiang et al. (2015) Dynamic Proteome Response of Pseudomonas aeruginosa to Tobramycin Antibiotic Treatment. Mol Cell Proteomics 14:2126-37|
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