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

Nina C. Hubner, Luan N. Nguyen, Nadine C. Hornig, Hendrik G. Stunnenberg

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)

Abstract

Interactions between transcription factors and genomic DNA, and in particular their impact on disease and cell fate, have been extensively studied on a global level using techniques based on next-generation sequencing. These approaches, however, do not allow an unbiased study of protein complexes that bind to certain DNA sequences. DNA pulldowns from crude lysates combined with quantitative mass spectrometry were recently introduced to close this gap. Established protocols, however, are restricted to cell lines because they are based on metabolic labeling or require large amounts of material. We introduce a high-throughput-compatible DNA pulldown 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 consensus DNA motifs in extracts from primary foreskin fibroblasts and peripheral blood mononuclear cells (PBMCs) freshly isolated from human donors. Nuclear proteomes with absolute quantification of nearly 7000 proteins in K562 cells and PBMCs clearly link differential interactions to differences in protein abundance, hence stressing the importance of selecting relevant cell extracts for any interaction in question. As shown for rs6904029, a SNP highly associated with chronic lymphocytic leukemia, our approach can provide invaluable functional data, for example, through integration with GWAS.

Original languageEnglish
Pages (from-to)1315-1329
Number of pages15
JournalJournal of Proteome Research
Volume14
Issue number2
DOIs
Publication statusPublished - 6 Feb 2015
Externally publishedYes

Keywords

  • dimethyl labeling
  • DNA pulldown
  • DNA-protein interactions
  • protein complex
  • quantitative mass spectrometry
  • transcription factor

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