Analyzing protein-protein interactions by quantitative mass spectrometry

Cell Signaling and Mass Spectrometry Group, Max Delbrück Center for Molecular Medicine, Berlin, Germany.
Methods (Impact Factor: 3.65). 03/2011; 54(4):387-95. DOI: 10.1016/j.ymeth.2011.03.001
Source: PubMed


Since most cellular processes depend on interactions between proteins, information about protein-protein interactions (PPIs) provide valuable insights into protein function. Over the last years, quantitative affinity purification followed by mass spectrometry (q-AP-MS) has become a powerful approach to investigate PPIs in an unbiased manner. In q-AP-MS the protein of interest is biochemically enriched together with its interaction partners. In parallel, a control experiment is performed to control for non-specific binding. Quantitative mass spectrometry is then employed to compare protein levels in both samples and to exclude non-specific contaminants. Here, we provide two detailed q-AP-MS protocols for pull-downs with immobilized bait proteins or transient transfection of tagged expression constructs. We discuss benefits and limitations of q-AP-MS and highlight critical parameters that need to be considered. The protocols and background information presented here allow the reader to adapt the generic q-AP-MS strategy for a wide range of biological questions.

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    • "Proteins often functionally interact as part of complex and dynamic networks [1] [2]. An important goal of current proteomics research is to develop techniques that can accurately identify such protein 'interactomes' [3]. A popular method uses epitope-tagged proteins in pull-down assays. "
    Dataset: SILAC-iPAC

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    • "We conclude that in the absence of Insm1, endocrine progenitor cells of the pituitary are correctly specified, but their differentiation is disrupted. Insm1 interacts with Kdm1a, Rcor1 and Hdac1/2 via its SNAG domain To discover Insm1-interacting proteins, we combined stable isotope labeling by amino acids in cell culture (SILAC) and affinity purification, an approach that can identify protein-protein interactions with very high confidence (Selbach and Mann, 2006; Paul et al., 2011). This technology relies on the quantification of proteins that co-immunoprecipitate with Insm1. "
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    ABSTRACT: The Insm1 gene encodes a zinc finger factor expressed in many endocrine organs. We show here that Insm1 is required for differentiation of all endocrine cells in the pituitary. Thus, in Insm1 mutant mice, hormones characteristic of the different pituitary cell types (thyroid-stimulating hormone, follicle-stimulating hormone, melanocyte-stimulating hormone, adrenocorticotrope hormone, growth hormone and prolactin) are absent or produced at markedly reduced levels. This differentiation deficit is accompanied by upregulated expression of components of the Notch signaling pathway, and by prolonged expression of progenitor markers, such as Sox2. Furthermore, skeletal muscle-specific genes are ectopically expressed in endocrine cells, indicating that Insm1 participates in the repression of an inappropriate gene expression program. Because Insm1 is also essential for differentiation of endocrine cells in the pancreas, intestine and adrenal gland, it is emerging as a transcription factor that acts in a pan-endocrine manner. The Insm1 factor contains a SNAG domain at its N-terminus, and we show here that the SNAG domain recruits histone-modifying factors (Kdm1a, Hdac1/2 and Rcor1-3) and other proteins implicated in transcriptional regulation (Hmg20a/b and Gse1). Deletion of sequences encoding the SNAG domain in mice disrupted differentiation of pituitary endocrine cells, and resulted in an upregulated expression of components of the Notch signaling pathway and ectopic expression of skeletal muscle-specific genes. Our work demonstrates that Insm1 acts in the epigenetic and transcriptional network that controls differentiation of endocrine cells in the anterior pituitary gland, and that it requires the SNAG domain to exert this function in vivo.
    Development 11/2013; 140(24). DOI:10.1242/dev.097642 · 6.46 Impact Factor
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    • "We performed immuno-based affinity purification experiments followed by mass spectrometric protein identification (46) using the E13-inducible ES clones with the 3xFLAG (‘Materials and Methods’ section). We identified 23 potential protein partners of E13 with high-confidence (Table 2), among which there are two TFs [Gtf2e2, Btf3 (47)], several mRNA processing proteins and two components of the Polycomb complex [Eed, Suz12 (48) and the retinoblastoma binding protein Rbbp4, which may be involved in pluripotent stem cell maintenance and neuronal differentiation (49)]. "
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    ABSTRACT: Gene expression profiles can be used to infer previously unknown transcriptional regulatory interaction among thousands of genes, via systems biology 'reverse engineering' approaches. We 'reverse engineered' an embryonic stem (ES)-specific transcriptional network from 171 gene expression profiles, measured in ES cells, to identify master regulators of gene expression ('hubs'). We discovered that E130012A19Rik (E13), highly expressed in mouse ES cells as compared with differentiated cells, was a central 'hub' of the network. We demonstrated that E13 is a protein-coding gene implicated in regulating the commitment towards the different neuronal subtypes and glia cells. The overexpression and knock-down of E13 in ES cell lines, undergoing differentiation into neurons and glia cells, caused a strong up-regulation of the glutamatergic neurons marker Vglut2 and a strong down-regulation of the GABAergic neurons marker GAD65 and of the radial glia marker Blbp. We confirmed E13 expression in the cerebral cortex of adult mice and during development. By immuno-based affinity purification, we characterized protein partners of E13, involved in the Polycomb complex. Our results suggest a role of E13 in regulating the division between glutamatergic projection neurons and GABAergic interneurons and glia cells possibly by epigenetic-mediated transcriptional regulation.
    Nucleic Acids Research 11/2012; 41(2). DOI:10.1093/nar/gks1136 · 9.11 Impact Factor
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