Dataset Information

Combining quantitative proteomics and interactomics for a deeper insight into molecular differences between human cell lines

ABSTRACT: 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.

INSTRUMENT(S): Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Cell Culture

DISEASE(S): Cervix Carcinoma,Breast Cancer

SUBMITTER: Anna Bakhtina

LAB HEAD: Jim Bruce

PROVIDER: PXD052801 | Pride | 2024-09-16

REPOSITORIES: Pride

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			Action	DRS
	062123_cell_lines_DIA_24mz_HEK293_1.mzML	Mzml
	062123_cell_lines_DIA_24mz_HEK293_2.mzML	Mzml
	062123_cell_lines_DIA_24mz_HEK293_3.mzML	Mzml
	062123_cell_lines_DIA_24mz_HeLa_1.mzML	Mzml
	062123_cell_lines_DIA_24mz_HeLa_2.mzML	Mzml

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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.

Project description:All classes of RNA undergo processing. The two largest RNA processing complexes are those involved in rRNA biogenesis and mRNA splicing. Because of their complexity and essential roles, it has been difficult to dissect the functional interactions within these machines as well as to identify possible cross-talk with other steps in gene expression. Here we report the results of a high-density, quantitative genetic interaction map comprising 107,155 individual interactions involving 552 mutants, 166 of which are hypomorphic alleles of essential genes (This data is available at The Krogan Lab Interactome Database). Our data allow unique insights into the functional organization of the RNA processing machines. For example, we identify unexpected connections involving a component of the 19S proteasome, Sem1/Dss1. We show that Sem1 has genetic, physical and functional interactions with the mRNA export complex, Thp1-Sac3; it also interacts physically with a putative component of the COP9 signalosome, Csn12. We show using splicing-specific microarrays that Csn12 has an unanticipated role in mRNA splicing. Finally, we demonstrate the utility of this dataset in determining functions for uncharacterized ORFs. We show that deletion of the uncharacterized YNR004W causes a very similar splicing defect to that caused by deletion of TGS1, the enzyme that trimethylates the caps of snRNAs and snoRNAs. Keywords: E-MAP, RNA Processing, splicing, splicing-specific microarray, CSN12, COP9 signalosome Splicing-specific microarrays were used to assay the changes to splicing caused by deletion of several non-essential genes which had no previously characterized role in mRNA splicing but were suggested to be involved in this process by high-throughput genetic and/or protein-protein interaction data in Saccharomyces cerevisiae. The data includes samples collected from some mutants in log phase growth at 30 degrees C and from some mutants shifted to 16 degrees C, all competitively hybridized against wild type samples collected under the same conditions. .

Project description:Classical methods of investigating protein-protein interactions (PPIs) are generally performed in non-living systems, yet in recent years new technologies utilizing proximity labeling (PL) have given researchers the tools to explore proximal PPIs in living systems. PL has distinct advantages over traditional protein interactome studies, such as the ability to identify weak and transient interactions in vitro and in vivo. Most PL studies are performed on targets within or on the cell membrane. We have adapted the original PL method to investigate PPIs within the extracellular compartment, using both BioID2 and TurboID, that we term extracellular PL (ePL). To demonstrate the utility of this modified technique, we investigate the interactome of the widely expressed matrisome protein Tissue inhibitors of metalloproteinases 2 (TIMP2). Tissue inhibitors of metalloproteinases (TIMPs) are a family of multi-functional proteins that were initially defined by their ability to inhibit the enzymatic activity of metalloproteinases (MPs), the major mediators of extracellular matrix (ECM) breakdown and turnover. TIMP2 exhibits a broad expression profile and is often abundant in both normal and diseased tissues. Understanding the functional transformation of matrisome regulators, like TIMP2, during the evolution of tissue microenvironments associated with disease progression is essential for the development of ECM targeted therapeutics. Using carboxyl- and amino-terminal fusion proteins of TIMP2 with BioID2 and TurboID, we describe the TIMP2 proximal interactome. We also illustrate how the TIMP2 interactome changes in the presence of different stimuli, in different cell types, in unique culture conditions (2D vs 3D), and with different reaction kinetics (BioID2 vs. TurboID); demonstrating the power of this technique versus classical PPI methods. We propose that the screening of matrisome targets in disease models using ePL will reveal new therapeutic targets for further comprehensive studies.

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Combining quantitative proteomics and interactomics for a deeper insight into molecular differences between human cell lines

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