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ChromID identifies the protein interactome at chromatin marks

Nature Biotechnology volume 38, pages 728–736 (2020)Cite this article

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Abstract

Chromatin modifications regulate genome function by recruiting proteins to the genome. However, the protein composition at distinct chromatin modifications has yet to be fully characterized. In this study, we used natural protein domains as modular building blocks to develop engineered chromatin readers (eCRs) selective for DNA methylation and histone tri-methylation at H3K4, H3K9 and H3K27 residues. We first demonstrated their utility as selective chromatin binders in living cells by stably expressing eCRs in mouse embryonic stem cells and measuring their subnuclear localization, genomic distribution and histone-modification-binding preference. By fusing eCRs to the biotin ligase BASU, we established ChromID, a method for identifying the chromatin-dependent protein interactome on the basis of proximity biotinylation, and applied it to distinct chromatin modifications in mouse stem cells. Using a synthetic dual-modification reader, we also uncovered the protein composition at bivalently modified promoters marked by H3K4me3 and H3K27me3. These results highlight the ability of ChromID to obtain a detailed view of protein interaction networks on chromatin.

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Fig. 1: Nuclear and genomic localization indicate correct eCR interactions with the genome.
Fig. 2: Functional analysis indicates dependency on reader domains and modifications for correct eCR localization.
Fig. 3: ChromID identifies proteins associated with H3K9me3 and DNA methylation.
Fig. 4: Generation and validation of eCRs reading bivalent H3K4me3 and H3K27me3 marks.
Fig. 5: ChromID identifies the proteins associated with key chromatin marks in mESCs.

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Data availability

All sequencing datasets produced in this study have been deposited in the NCBI Gene Expression Omnibus under accession number GSE128907. The mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD014483 and PXD017235. Additional genomics datasets used in this study include U2OS FLASH77 (GSE69149); U2OS H3K4me3 (ref. 78) (GSE87831); mESC whole-genome bisulfite sequencing79 (GSE30206); mESC TAF3 (ref. 80) (GSE30959); mESC CBX7 ref. 81) (GSE42466); mESC CBX1 (ref. 27) (GSE71114); mESC bio-tagged MBD1 (ref. 22) (GSE39610); and mESC H4K20me3 from ENCODE (SRR094944).

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Acknowledgements

We thank D. Schübeler (FMI, Basel) for providing the Dnmt-TKO embryonic stem cell line and P. A. Khavari (Stanford) for cDNA encoding the BASU biotin ligase. Furthermore, we thank B. Roschitzki, J. Grossmann, T. Kockmann, P. Knobel, M. Majchrzak and R. Klemm for initial discussions on biotin-ID methods and MS detection. We would like to thank members of the Functional Genomics Center Zurich for high-throughput sequencing and MS support, members of the Centre for Microscopy and Image Analysis for their support and the Science IT team at the University of Zurich for providing the computational infrastructure. We are grateful to the Edinburgh Protein Production Facility for their support. We thank V. Major for help with cloning and K. Webb for help with histone purification. Furthermore, we thank M. Altmeyer, A. Krebs, D. Schübeler and members of the Baubec laboratory for their critical input on the manuscript. The authors would like to acknowledge the following support: Swiss National Science Foundation 157488 and 180345 to T.B. and 107679 to R.A.; Swiss Initiative in Systems Biology (SystemsX.ch) 2015/322 to T.B.; ERC-AdvGr 670821-Proteomics 4D to R.A.; ERC-STG 639253 to P.V.; Innovative Medicines Initiative (EU/EFPIA) ULTRA-DD grant 115766 to R.A; Wellcome Trust (104175/Z/14/Z, Sir Henry Dale Fellowship) to P.V.; EMBO long-term fellowships to N.S. and C.F.; a UZH Forschungskredit fellowship to N.S.; a UZH Candoc fellowship to C.A.; Wellcome Trust Doctoral Studentship 105244 to E.B.; and Wellcome Trust core funding 203149 and 101527/Z/13/Z to Wellcome Centre for Cell Biology and Edinburgh Protein Production Facility, respectively.

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Authors and Affiliations

  1. Department of Molecular Mechanism of Disease, University of Zurich, Zurich, Switzerland

    Rodrigo Villaseñor, Ramon Pfaendler, Christina Ambrosi, Stefan Butz, Sara Giuliani, Nina Schmolka, Massimiliano Manzo, Joël Wirz & Tuncay Baubec

  2. Life Science Zurich Graduate School, University of Zurich and ETH Zurich, Zurich, Switzerland

    Christina Ambrosi, Stefan Butz, Annika L. Gable & Massimiliano Manzo

  3. Wellcome Centre for Cell Biology, School of Biological Sciences, University of Edinburgh, Edinburgh, UK

    Elana Bryan, Thomas W. Sheahan & Philipp Voigt

  4. Institute of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland

    Annika L. Gable & Christian von Mering

  5. Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland

    Christian Feller & Ruedi Aebersold

  6. Faculty of Science, University of Zurich, Zurich, Switzerland

    Ruedi Aebersold

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  1. Rodrigo Villaseñor
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Contributions

R.V. and T.B. conceived and designed the study. R.V., R.P., N.S., M.M. and T.B. developed tools and protocols. R.V., R.P., S.B., S.G., C.A. and J.W. generated cell lines and performed experiments. C.F. and R.A. designed and performed LC–MS experiments, analyzed data and interpreted results for histone PTM detection. A.L.G. performed STRING network analysis with supervision from C.v.M. E.B. and T.S. performed nucleosome reconstitution and interaction experiments under supervision from P.V. R.V., R.P., M.M. and T.B. analyzed data. R.V. and T.B. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Tuncay Baubec.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–15

Reporting Summary

Supplementary Table 1

Dentified proximal interactome at H3K4me3, H3K27me3, H3K9me3 and bivalently marked H3K4me3/H3K27me3 sites. Identified proteins from ChromID experiments using histone modification eCRs, including log2FC, P values and a Boolean indicator if called significant.

Supplementary Table 2

Identified proximal interactome at 5-methyl-CpG DNA methylated sites. Identified proteins from ChromID experiments using the 5-methyl-CpG-specific eCR, including log2FC, P values and a Boolean indicator if called significant.

Supplementary Table 3

Estimation of protein abundance by data-independent acquisition in eCR-expressing cells. Protein abundance is based on average intensities calculated from the top three peptides per protein.

Supplementary Video 1

Time-lapse fluorescence imaging of proliferating eGFP+ control cells. Cells undergoing division at one selected region were imaged at 5‐min intervals for approximately 12.5 h. The video was generated using FIJI (version 2.0.0) and the Bio-Formats Importer plugin. An appropriate xy region and a single z plane were selected for further image series analysis and display. Scale bar, 5 µm.

Supplementary Video 2

Time-lapse fluorescence imaging of proliferating H3K27me3-reader cells. Cells undergoing division at one selected region were imaged at 5‐min intervals for approximately 12.5 h. The video was generated using FIJI (version 2.0.0) and the Bio-Formats Importer plugin. An appropriate xy region and a single z plane were selected for further image series analysis and display. Scale bar, 5 µm.

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Villaseñor, R., Pfaendler, R., Ambrosi, C. et al. ChromID identifies the protein interactome at chromatin marks. Nat Biotechnol 38, 728–736 (2020). https://doi.org/10.1038/s41587-020-0434-2

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