Dataset Information

Cellular interactome dynamics during paclitaxel treatment

ABSTRACT: Cell cycle inhibitors, including paclitaxel, are among the most widely used and effective cancer therapies. However, several challenges limit the success of paclitaxel including drug resistance and toxic side effects. Paclitaxel is thought to act primarily by stabilizing microtubules and locking cells in a mitotic state. However, the resulting cytotoxicity and tumor shrinkage rates observed cannot be fully explained by this mechanism alone. Here we apply quantitative chemical cross-linking with mass spectrometry analysis to paclitaxel treated cells. Our results provide large scale measurements of relative protein levels and, perhaps more importantly, changes to protein conformations and interactions that occur upon paclitaxel treatment. Drug concentration dependent changes are revealed in known drug targets including tubulins, as well as many other proteins and protein complexes involved in apoptotic signaling and cellular homeostasis. As such this study provides insight into systems-level changes to protein structures and interactions which occur with paclitaxel treatment.

INSTRUMENT(S): LTQ FT, Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Epithelial Cell

DISEASE(S): Cervix Carcinoma

SUBMITTER: Juan Chavez

LAB HEAD: James E. Bruce

PROVIDER: PXD015751 | Pride | 2019-11-21

REPOSITORIES: Pride

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Dataset's files

Source:

			Action	DRS
	021518_HeLa_L10nMTXL_HD_F10_4hr_1.mzXML	Mzxml
	021518_HeLa_L10nMTXL_HD_F10_4hr_1.raw	Raw
	021518_HeLa_L10nMTXL_HD_F10_4hr_1.raw.txt	Raw
	021518_HeLa_L10nMTXL_HD_F10_4hr_2.mzXML	Mzxml
	021518_HeLa_L10nMTXL_HD_F10_4hr_2.raw	Raw

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Publications

Cellular Interactome Dynamics during Paclitaxel Treatment.

Chavez Juan D JD Keller Andrew A Zhou Bo B Tian Rong R Bruce James E JE

Cell reports 20191101 8

Cell-cycle inhibitors, including paclitaxel, are among the most widely used and effective cancer therapies. However, several challenges limit the success of paclitaxel, including drug resistance and toxic side effects. Paclitaxel is thought to act primarily by stabilizing microtubules, locking cells in a mitotic state. However, the resulting cytotoxicity and tumor shrinkage rates observed cannot be fully explained by this mechanism alone. Here we apply quantitative chemical cross-linking with ma ...[more]

PMID: 31747606

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Project description:Cellular functional pathways have evolved through selection based on fitness benefits conferred through protein intra- and inter-molecular interactions that comprise all protein conformational features and protein-protein interactions, collectively referred to as the interactome. While the interactome is regulated by proteome levels, it is also regulated independently by, post translational modification, co-factor, and ligand levels, as well as local protein environmental factors, such as osmolyte concentration, pH, ionic strength, temperature and others. In modern biomedical research, cultivatable cell lines have become an indispensable tool, with selection of optimal cell lines that exhibit specific functional profiles being critical for success in many cases. While it is clear that cell lines derived from different cell types have differential proteome levels, increased understanding of large-scale functional differences requires additional information beyond abundance level measurements, including how protein conformations and interactions are altered in certain cell types to shape functional landscapes. Here, we employed quantitative in vivo protein cross-linking coupled to mass spectrometry to probe large-scale protein conformational and interaction changes among three commonly employed human cell lines, HEK293, MCF-7, and HeLa cells. Isobaric quantitative Protein Interaction Reporter (iqPIR) technologies were used to obtain quantitative values of cross-linked peptides across three cell lines. These data illustrated highly reproducible (R2 values larger than 0.8 for all biological replicates) quantitative interactome levels across multiple biological replicates. We also measured protein abundance levels in these cells using data independent acquisition quantitative proteomics methods. Combining quantitative interactome and proteomics information allowed visualization of cell type-specific interactome changes mediated by proteome level adaptations as well as independently regulated interactome changes to gain deeper insight into possible drivers of these changes. Among the biggest detected alterations in protein interactions and conformations are changes in cytoskeletal proteins, RNA-binding proteins, chromatin remodeling complexes, mitochondrial proteins, and others. Overall, these data demonstrate the utility and reproducibility of quantitative cross-linking to study systems-level interactome variations. Moreover, these results illustrate how combined quantitative interactomics and proteomics can provide unique insight on cellular functional landscapes.