MassIVE MSV000079550

Imported Reanalysis Dataset Public PXD000782

A quantitative proteomics tool to identify DNA-protein interactions in primary cells or blood

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Description

The interaction between transcription factors and genomic DNA forms the basis for spatio-temporal control of gene expression. Therefore, these interactions and their impact on disease and cellular fate are extensively studied on a global level, mainly using techniques based on next-generation sequencing. These techniques, however, do not allow an unbiased study of proteins or entire protein complexes that bind to a certain DNA sequence. In recent years, DNA pull-downs followed by quantitative mass spectrometry were introduced to close this gap. Established protocols, however, are based on metabolic labeling techniques or require enormous amounts of cellular material, thus restricting the method to cell lines grown in culture. Furthermore, they require substantial amount of expertise, thus keeping this technique restricted to a limited number of laboratories. Here, we introduce a high-throughput compatible, LC-MS/MS based DNA pull-down that combines on-bead digestion with direct dimethyl labeling or label-free protein quantification. We demonstrate, that our method can efficiently identify transcription factors binding to their known consensus DNA motifs when using nuclear extracts from model cell lines. Subsequently, we apply the method to study DNA-protein interactions in primary foreskin fibroblasts and peripheral blood mononuclear cells (PBMCs) freshly isolated from human donors. We show that the same DNA sequence binds different sets of proteins in an established model cell line as opposed to PBMCs. This stresses the importance of selecting relevant cell extracts for any interaction in question. In-depth nuclear proteomes with absolute quantification of close to 7,000 proteins in K562 cells and PBMCs clearly link these differential interactions to differences in protein abundance. In conclusion, our approach, applicable to primary material and capable of profiling DNA-protein interactions in high-throughput, will likely prove itself as a useful screening platform and will provide invaluable functional data, for example through integration with large scale GWAS. [dataset license: CC0 1.0 Universal (CC0 1.0)]

Keywords: DNA pull-down ; blood ; primary cells ; PBMCs

Contact

Principal Investigators:
(in alphabetical order) 		Dr Hendrik G. Stunnenberg
Submitting User: ccms

Publications

Hubner NC, Nguyen LN, Hornig NC, Stunnenberg HG.
A quantitative proteomics tool to identify DNA-protein interactions in primary cells or blood.
J. Proteome Res. 2015 Feb 6;14(2):1315-29. Epub 2015 Jan 8.

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Identification Results
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Quantification Results
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Number of distinct conditions across all analyses (original submission and reanalyses) associated with this dataset.

Distinct condition labels are counted across all files submitted in the "Metadata" category having a "Condition" column in this dataset.

"N/A" means no results of this type were submitted.
Number of distinct biological replicates across all analyses (original submission and reanalyses) associated with this dataset.

Distinct replicate labels are counted across all files submitted in the "Metadata" category having a "BioReplicate" or "Replicate" column in this dataset.

"N/A" means no results of this type were submitted.
Number of distinct technical replicates across all analyses (original submission and reanalyses) associated with this dataset.

The technical replicate count is defined as the maximum number of times any one distinct combination of condition and biological replicate was analyzed across all files submitted in the "Metadata" category. In the case of fractionated experiments, only the first fraction is considered.

"N/A" means no results of this type were submitted.
Originally identified proteins that were automatically remapped by MassIVE to proteins in the SwissProt human reference database.

"N/A" means no results of this type were submitted.
Number of distinct protein accessions reported across all analyses (original submission and reanalyses) associated with this dataset.

"N/A" means no results of this type were submitted.
Number of distinct unmodified peptide sequences reported across all analyses (original submission and reanalyses) associated with this dataset.

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

"N/A" means no results of this type were submitted.
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.
This dataset may not contain all raw spectra data as originally deposited in PRIDE. It has been imported to MassIVE for reanalysis purposes, so its spectra data here may consist solely of processed peak lists suitable for reanalysis with most software.