Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2018 Jun 13;9(1):2311.
doi: 10.1038/s41467-018-04619-5.

A Quantitative Mass Spectrometry-Based Approach to Monitor the Dynamics of Endogenous Chromatin-Associated Protein Complexes

Affiliations
Free PMC article

A Quantitative Mass Spectrometry-Based Approach to Monitor the Dynamics of Endogenous Chromatin-Associated Protein Complexes

Evangelia K Papachristou et al. Nat Commun. .
Free PMC article

Abstract

Understanding the dynamics of endogenous protein-protein interactions in complex networks is pivotal in deciphering disease mechanisms. To enable the in-depth analysis of protein interactions in chromatin-associated protein complexes, we have previously developed a method termed RIME (Rapid Immunoprecipitation Mass spectrometry of Endogenous proteins). Here, we present a quantitative multiplexed method (qPLEX-RIME), which integrates RIME with isobaric labelling and tribrid mass spectrometry for the study of protein interactome dynamics in a quantitative fashion with increased sensitivity. Using the qPLEX-RIME method, we delineate the temporal changes of the Estrogen Receptor alpha (ERα) interactome in breast cancer cells treated with 4-hydroxytamoxifen. Furthermore, we identify endogenous ERα-associated proteins in human Patient-Derived Xenograft tumours and in primary human breast cancer clinical tissue. Our results demonstrate that the combination of RIME with isobaric labelling offers a powerful tool for the in-depth and quantitative characterisation of protein interactome dynamics, which is applicable to clinical samples.

Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
The qPLEX-RIME workflow. Proteins in treated and un-treated cell cultures at different time points or in variable genomic backgrounds (e.g. different cell lines or mutated conditions) are double-crosslinked and cell nuclei are isolated and sonicated (a, b). Target protein complexes are immunoprecipitated and subjected to on-bead trypsin digestion (c, d). The generated peptides are labelled using different TMT reagents and pooled in a single mixture, which is fractionated using Reversed-Phase cartridges (d). Peptide fractions are analysed with the MultiNotch MS3 method (e) followed by data processing and statistical analysis using novel analytic suite (qPLEXanalyzer) (f)
Fig. 2
Fig. 2
Application of qPLEX-RIME for the identification of ERα-specific interactors in breast cancer cell lines. a Bar plots illustrating the overlap of the qPLEX-RIME data with known ERα-associated proteins from BioGRID and STRING databases. b Volcano plot summarising the quantitative results of the ERα qPLEX-RIME. ERα and several of its known significantly enriched interactors are labelled. c Sequence coverage of the ERα protein in the qPLEX-RIME analysis. d Boxplots of the number of unique peptides for the overlapping ERα-associated proteins between the non-quantitative RIME and the qPLEX-RIME method. Centre line shows the median, bounds of box correspond to the first and third quartiles and the upper and lower whiskers extend to the largest or the smallest value no further than 1.5 × IQR (inter-quartile range)
Fig. 3
Fig. 3
Application of qPLEX-RIME in CREBBP, NCOA3 and POLR2A. a Peptide sequence coverage of the CREBBP (CBP) protein in the qPLEX-RIME analysis (top panel). The volcano plot summarises the quantitative results of the CBP qPLEX-RIME and several of its known interactors are highlighted in red font (bottom panel). b Peptide sequence coverage of the NCOA3 protein in the qPLEX-RIME analysis (top panel). The volcano plot summarises the quantitative results of the NCOA3 qPLEX-RIME and several of its known interactors are highlighted in red font (bottom panel). c Peptide sequence coverage of the phospho-polymerase II (POLR2A) protein in the qPLEX-RIME analysis (top panel). The volcano plot summarises the quantitative results of the POLR2A qPLEX-RIME and several of its known interactors are highlighted in red font (bottom panel)
Fig. 4
Fig. 4
Temporal profiling of the ERα interactome following treatment of MCF7 cells with OHT. a Volcano plots highlighting enriched or lost proteins in the ERα interactome upon OHT treatment for 2 h, 6 h and 24 h. b Boxplots illustrating the loss of ERα co-activator proteins CREBBP, NCOA3 and NRIP1 at 2 h and the enrichment of several subunits of the SWI/SNF and NuRD complexes at 6 h (left to right). Quantitative values are normalised so that the median of the vehicle treated samples is zero (centered around the median of vehicle). Centre line shows the median, bounds of box correspond to the first and third quartiles and the upper and lower whiskers extend to the largest or the smallest value no further than 1.5 × IQR (inter-quartile range)
Fig. 5
Fig. 5
Comparison of qPLEX-RIME data with total proteome analysis and RNA-seq data. a Line plots of the significantly enriched or lost proteins in the qPLEX-RIME data (top panel), their respective profiles in the total proteome analysis (middle panel) and in the RNA-seq analysis (bottom panel). b Line plots representing two k-means clusters of down- and upregulated proteins identified in total proteome analysis (top 10% most variable proteins with at least one significant change, adj. p-value < 0.05). c Gene set enrichment analysis for the down- and upregulated protein clusters
Fig. 6
Fig. 6
Characterisation of ERα interactors from in vivo samples. a Schematic representation of the ERα qPLEX-RIME workflow in human xenograft tissues or in human breast cancer tumours. b Volcano plot summarising the quantitative results of the ERα interactome in human breast cancer tumours. Several well-known ERα interactors are labelled. c Boxplots illustrating the enrichment of selected known ERα interactors in the ERα samples compared to IgG controls in human breast cancer tissues. The log2 values are normalised so that the median of IgGs is zero. Centre line shows the median, bounds of box correspond to the first and third quartiles and the upper and lower whiskers extend to the largest or the smallest value no further than 1.5 × IQR (inter-quartile range)
Fig. 7
Fig. 7
Most frequently enriched ERα interactors. STRING network of 253 ERα interactors identified consistently across all the qPLEX-RIME analyses performed in MCF7 cells. The font size increases proportionally to the average fold-change enrichment of these proteins across all the ERα samples compare to IgG controls

Similar articles

See all similar articles

Cited by 13 articles

See all "Cited by" articles

References

    1. Vidal M, Cusick ME, Barabasi AL. Interactome networks and human disease. Cell. 2011;144:986–998. doi: 10.1016/j.cell.2011.02.016. - DOI - PMC - PubMed
    1. Ewing RM, et al. Large-scale mapping of human protein–protein interactions by mass spectrometry. Mol. Syst. Biol. 2007;3:89. doi: 10.1038/msb4100134. - DOI - PMC - PubMed
    1. Stelzl U, et al. A human protein–protein interaction network: a resource for annotating the proteome. Cell. 2005;122:957–968. doi: 10.1016/j.cell.2005.08.029. - DOI - PubMed
    1. Rolland T, et al. A proteome-scale map of the human interactome network. Cell. 2014;159:1212–1226. doi: 10.1016/j.cell.2014.10.050. - DOI - PMC - PubMed
    1. Rual JF, et al. Towards a proteome-scale map of the human protein–protein interaction network. Nature. 2005;437:1173–1178. doi: 10.1038/nature04209. - DOI - PubMed

Publication types

MeSH terms

Feedback