MassIVE MSV000081520

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Systematic analysis of human nuclear protein complexes by quantitative mass spectrometry profiling

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Description

Many proteins form stable complexes that coordinate diverse and complex cellular processes. Systematic analysis of protein complexes provides insights into how the ensemble of expressed proteome is organized into functional units. Protein complexes are usually identified using either yeast-two-hybrid (Y2H) or affinity purification-mass spectrometry (AP-MS); however, these methods are low throughput and require genetic manipulation. While there have been advances in techniques for proteome-wide profiling of cytoplasmic protein complexes, information about human nuclear protein complexes are very limited. To close this gap, we combined native size exclusion chromatographic (SEC) with label-free quantitative liquid chromatography tandem-MS (LC-MS/MS) profiling to characterize hundreds of nuclear protein complexes isolated from human glioblastoma multiforme (GBM) cells. We identified 1794 proteins that overlapped between two biological replicates, of which 1244 proteins were characterized as existing within stably associated putative complexes. We demonstrated a high degree of SEC reproducibility by comparing protein elution profiles between the two independent fractionation procedures, and co-elution of several chromatin remodeling and DNA damage repair complexes was confirmed by independent co-immunoprecipitation (co-IP) and immunoblot analyses. Co-IP experiments confirmed the interaction of PARP1 with Ku70/Ku80 proteins, and between HDAC1 and CHD4. HDAC1/2 also co-migrated with various SIN3a and NuRD components in SEC fractionation including SIN3A, SAP30, RBBP4, RBBP7, and NCOR1. Co-elution of HDAC1/2/3 with both the KDM1A and RCOR1 further confirmed that these proteins are integral components of human deacetylase complexes. Our approach also demonstrated the ability to identify potential moonlighting complexes and novel complexes containing uncharacterized proteins. Overall, the results demonstrated the utility of SEC fractionation and LC-MS analysis for system-wide profiling of proteins to predict the existence of distinct forms of nuclear protein complexes. [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: size exclusion chromatography, proteomics, protein complex profiling, mass spectrometry, nuclear fraction

Contact

Principal Investigators:
(in alphabetical order) 		Uma K Aryal, Purdue University, United States
Submitting User: umakaryal
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Distinct condition labels are counted across all files submitted in the "Metadata" category having a "Condition" column in this dataset.

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Number of distinct biological replicates across all analyses (original submission and reanalyses) associated with this dataset.

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Number of distinct technical replicates across all analyses (original submission and reanalyses) associated with this dataset.

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Originally identified proteins that were automatically remapped by MassIVE to proteins in the SwissProt human reference database.

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Number of distinct protein accessions reported across all analyses (original submission and reanalyses) associated with this dataset.

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Number of distinct peptide sequences (including modified variants or peptidoforms) reported across all analyses (original submission and reanalyses) associated with this dataset.

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Total number of peptide-spectrum matches (i.e. spectrum identifications) reported across all analyses (original submission and reanalyses) associated with this dataset.

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Number of distinct proteins quantified across all analyses (original submission and reanalyses) associated with this dataset.

Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

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Number of distinct proteins found to be differentially abundant in at least one comparison across all analyses (original submission and reanalyses) associated with this dataset.

A protein is differentially abundant if its change in abundance across conditions is found to be statistically significant with an adjusted p-value <= 0.05 and lists no issues associated with statistical tests for differential abundance.

Distinct protein accessions are counted across all files submitted in the "Statistical Analysis of Quantified Analytes" category having a "Protein" column in this dataset.

"N/A" means no results of this type were submitted.
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