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• W2169107124 abstract "Article5 January 2015Open Access Source Data Deciphering preferential interactions within supramolecular protein complexes: the proteasome case Bertrand Fabre Bertrand Fabre CNRS, IPBS (Institut de Pharmacologie et de Biologie Structurale), Toulouse, France Université de Toulouse, UPS, IPBS, Toulouse, France Search for more papers by this author Thomas Lambour Thomas Lambour CNRS, IPBS (Institut de Pharmacologie et de Biologie Structurale), Toulouse, France Université de Toulouse, UPS, IPBS, Toulouse, France Search for more papers by this author Luc Garrigues Luc Garrigues CNRS, IPBS (Institut de Pharmacologie et de Biologie Structurale), Toulouse, France Université de Toulouse, UPS, IPBS, Toulouse, France Search for more papers by this author François Amalric François Amalric CNRS, IPBS (Institut de Pharmacologie et de Biologie Structurale), Toulouse, France Université de Toulouse, UPS, IPBS, Toulouse, France Search for more papers by this author Nathalie Vigneron Nathalie Vigneron Ludwig Institute for Cancer Research, Brussels, Belgium WELBIO (Walloon Excellence in Life Sciences and Biotechnology), Brussels, Belgium de Duve Institute, Université catholique de Louvain, Brussels, Belgium Search for more papers by this author Thomas Menneteau Thomas Menneteau CNRS, IPBS (Institut de Pharmacologie et de Biologie Structurale), Toulouse, France Université de Toulouse, UPS, IPBS, Toulouse, France Search for more papers by this author Alexandre Stella Alexandre Stella CNRS, IPBS (Institut de Pharmacologie et de Biologie Structurale), Toulouse, France Université de Toulouse, UPS, IPBS, Toulouse, France Search for more papers by this author Bernard Monsarrat Bernard Monsarrat CNRS, IPBS (Institut de Pharmacologie et de Biologie Structurale), Toulouse, France Université de Toulouse, UPS, IPBS, Toulouse, France Search for more papers by this author Benoît Van den Eynde Benoît Van den Eynde Ludwig Institute for Cancer Research, Brussels, Belgium WELBIO (Walloon Excellence in Life Sciences and Biotechnology), Brussels, Belgium de Duve Institute, Université catholique de Louvain, Brussels, Belgium Search for more papers by this author Odile Burlet-Schiltz Corresponding Author Odile Burlet-Schiltz CNRS, IPBS (Institut de Pharmacologie et de Biologie Structurale), Toulouse, France Université de Toulouse, UPS, IPBS, Toulouse, France Search for more papers by this author Marie-Pierre Bousquet-Dubouch Corresponding Author Marie-Pierre Bousquet-Dubouch CNRS, IPBS (Institut de Pharmacologie et de Biologie Structurale), Toulouse, France Université de Toulouse, UPS, IPBS, Toulouse, France Search for more papers by this author Bertrand Fabre Bertrand Fabre CNRS, IPBS (Institut de Pharmacologie et de Biologie Structurale), Toulouse, France Université de Toulouse, UPS, IPBS, Toulouse, France Search for more papers by this author Thomas Lambour Thomas Lambour CNRS, IPBS (Institut de Pharmacologie et de Biologie Structurale), Toulouse, France Université de Toulouse, UPS, IPBS, Toulouse, France Search for more papers by this author Luc Garrigues Luc Garrigues CNRS, IPBS (Institut de Pharmacologie et de Biologie Structurale), Toulouse, France Université de Toulouse, UPS, IPBS, Toulouse, France Search for more papers by this author François Amalric François Amalric CNRS, IPBS (Institut de Pharmacologie et de Biologie Structurale), Toulouse, France Université de Toulouse, UPS, IPBS, Toulouse, France Search for more papers by this author Nathalie Vigneron Nathalie Vigneron Ludwig Institute for Cancer Research, Brussels, Belgium WELBIO (Walloon Excellence in Life Sciences and Biotechnology), Brussels, Belgium de Duve Institute, Université catholique de Louvain, Brussels, Belgium Search for more papers by this author Thomas Menneteau Thomas Menneteau CNRS, IPBS (Institut de Pharmacologie et de Biologie Structurale), Toulouse, France Université de Toulouse, UPS, IPBS, Toulouse, France Search for more papers by this author Alexandre Stella Alexandre Stella CNRS, IPBS (Institut de Pharmacologie et de Biologie Structurale), Toulouse, France Université de Toulouse, UPS, IPBS, Toulouse, France Search for more papers by this author Bernard Monsarrat Bernard Monsarrat CNRS, IPBS (Institut de Pharmacologie et de Biologie Structurale), Toulouse, France Université de Toulouse, UPS, IPBS, Toulouse, France Search for more papers by this author Benoît Van den Eynde Benoît Van den Eynde Ludwig Institute for Cancer Research, Brussels, Belgium WELBIO (Walloon Excellence in Life Sciences and Biotechnology), Brussels, Belgium de Duve Institute, Université catholique de Louvain, Brussels, Belgium Search for more papers by this author Odile Burlet-Schiltz Corresponding Author Odile Burlet-Schiltz CNRS, IPBS (Institut de Pharmacologie et de Biologie Structurale), Toulouse, France Université de Toulouse, UPS, IPBS, Toulouse, France Search for more papers by this author Marie-Pierre Bousquet-Dubouch Corresponding Author Marie-Pierre Bousquet-Dubouch CNRS, IPBS (Institut de Pharmacologie et de Biologie Structurale), Toulouse, France Université de Toulouse, UPS, IPBS, Toulouse, France Search for more papers by this author Author Information Bertrand Fabre1,2, Thomas Lambour1,2, Luc Garrigues1,2, François Amalric1,2, Nathalie Vigneron3,4,5, Thomas Menneteau1,2, Alexandre Stella1,2, Bernard Monsarrat1,2, Benoît Van den Eynde3,4,5, Odile Burlet-Schiltz 1,2 and Marie-Pierre Bousquet-Dubouch 1,2 1CNRS, IPBS (Institut de Pharmacologie et de Biologie Structurale), Toulouse, France 2Université de Toulouse, UPS, IPBS, Toulouse, France 3Ludwig Institute for Cancer Research, Brussels, Belgium 4WELBIO (Walloon Excellence in Life Sciences and Biotechnology), Brussels, Belgium 5de Duve Institute, Université catholique de Louvain, Brussels, Belgium *Corresponding author. Tel: +33 561175547; E-mail: [email protected] *Corresponding author. Tel: +33 561175544; E-mail: [email protected] Molecular Systems Biology (2015)11:771https://doi.org/10.15252/msb.20145497 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions Figures & Info Abstract In eukaryotic cells, intracellular protein breakdown is mainly performed by the ubiquitin–proteasome system. Proteasomes are supramolecular protein complexes formed by the association of multiple sub-complexes and interacting proteins. Therefore, they exhibit a very high heterogeneity whose function is still not well understood. Here, using a newly developed method based on the combination of affinity purification and protein correlation profiling associated with high-resolution mass spectrometry, we comprehensively characterized proteasome heterogeneity and identified previously unknown preferential associations within proteasome sub-complexes. In particular, we showed for the first time that the two main proteasome subtypes, standard proteasome and immunoproteasome, interact with a different subset of important regulators. This trend was observed in very diverse human cell types and was confirmed by changing the relative proportions of both 20S proteasome forms using interferon-γ. The new method developed here constitutes an innovative and powerful strategy that could be broadly applied for unraveling the dynamic and heterogeneous nature of other biologically relevant supramolecular protein complexes. Synopsis A new approach for analyzing sub-complex composition by combining affinity purification and protein abundance correlation profiling by mass spectrometry is employed to characterize the dynamic and heterogeneous nature of human proteasome complexes. This novel strategy allows in-depth characterization of proteasome complexes and confident identification of specific protein interactions. Previously unknown preferential associations between proteasome sub-complexes are revealed and strongly suggest that interactions within proteasome particles do not occur randomly. The standard proteasome and immunoproteasome, two main 20S proteasome subtypes, preferentially associate with different subsets of proteins regulating their activities. In vivo modulation of 20S catalytic subunits using IFNγ stimulation confirms PA28αβ and PA200/PI31 as specific regulators of 20S immunoproteasome and 20S standard proteasome, respectively. Introduction The proteasome is a supramolecular protein machinery that is central to protein homeostasis. In all eukaryotic cells, it is involved in the selective degradation of most short-lived intracellular proteins (Hershko & Ciechanover, 1998; Glickman & Ciechanover, 2002), ensuring subtle modulation of gene expression, but also the removal of misfolded, aberrant (i.e. oxidized or mutated), or otherwise damaged proteins, avoiding cytotoxicity. In higher eukaryotes, it is critical to the immune response because it generates antigenic peptide precursors that can be recognized by cytotoxic T lymphocytes (CTLs) on MHC class I molecules. Proteasome particles are formed by the dynamic association of several sub-complexes, a 20S core particle (20S CP), either single or associated with one or two regulatory particles (RPs) of identical or different protein composition. Proteasome complexes thus display a high degree of heterogeneity. The 20S CP presents a α7β7β7α7 barrel-like structure and was shown to exist in the eukaryotic cell as four different subtypes, depending on the subsets of incorporated catalytic beta subunits. In the standard proteasome (sP20S), the two β rings each contain three standard catalytic subunits, β1, β2, and β5, which are replaced by distinct immunosubunits, β1i, β2i, and β5i, in the immunoproteasome (iP20S), respectively. Two intermediate 20S CP subtypes, β5i 20S proteasome (β5i P20S) and β1iβ5i 20S proteasome (β1iβ5i P20S), bearing a mixed incorporation of standard and immunosubunits, β1, β2, β5i and β1i, β2, β5i, respectively, have also been identified in a wide range of cell types and tissues (Guillaume et al, 2010). The catalytic subunits are responsible for the three proteasome proteolytic activities (trypsin like, chymotrypsin like, and caspase like), which can be modulated by the replacement of standard subunits by immunosubunits (Orlowski & Wilk, 2000; Basler et al, 2013). The iP20S is induced during the immune response in mammals but also exists in various amounts as constitutive proteasome complexes, depending on tissues or cell type (Dahlmann et al, 2000; Zoeger et al, 2006; Klare et al, 2007; Bousquet-Dubouch et al, 2008; Guillaume et al, 2010, 2012). The two α rings are located at the opposite ends of the proteolytic cavity and regulate the access of substrates to catalytic sites through gated pores of 13 Å in diameter. In mammals, gate opening can be efficiently triggered through the association of the 20S CP with four main different RPs, the 19S regulatory particle (19S RP), PA28αβ, PA28γ (otherwise known as 11S RPs), and PA200. One 20S CP can interact at its two sides with either two identical regulators or two different ones, thus forming hybrid proteasomes (Tanahashi et al, 2000). The most studied regulator, the 19S RP, is involved in the recognition, the unfolding, and the translocation of poly-ubiquitinated substrates into the 20S CP for degradation. In addition to the 19S RP, PA28αβ and PA28γ RPs are abundant 20S proteasome-associated regulators present in the cytosol and the nucleus, respectively (Drews et al, 2007; Fabre et al, 2013). They catalyze protein degradation through an ubiquitin-independent pathway, which still needs to be completely clarified (Stadtmueller & Hill, 2011; Kish-Trier & Hill, 2013). Although the binding constants between the different 20S subtypes and its different RPs are not known, the binding mode between the α-ring and RPs has been established precisely by numerous structural studies and was found to be shared among species (Stadtmueller & Hill, 2011; Beck et al, 2012; da Fonseca et al, 2012; Lander et al, 2012; Lasker et al, 2012; Kish-Trier & Hill, 2013). In all cases, it involves the C-termini of RP subunits and a pocket at the interface between α-subunits. Recently, in-solution NMR surveys have evidenced an allosteric pathway linking the binding sites of C-termini of the 11S RP with the active sites of the Thermoplasma acidophilum 20S CP, emphasizing a clear connection between these regions that are 80 Å apart. In particular, the modification of active sites in the T. acidophilum CP was shown to induce structural changes at the α-ring binding interface (Ruschak & Kay, 2012). This clearly suggests that changes in active sites configurations, as found in sP20S and iP20S, might affect binding affinities for RPs. It would thus be of great interest to characterize proteasome heterogeneity and to determine whether preferential associations within proteasome sub-complexes do exist. Affinity purification coupled to mass spectrometry (AP-MS) is a very powerful and sensitive approach for the determination of protein complexes composition through the specific capture of a target protein and all associated partners (Gingras et al, 2007; Trinkle-Mulcahy et al, 2008; Glatter et al, 2009). A few studies take advantage of the quantitative nature of affinity purification associated with mass spectrometry (AP-MS) data to define network architecture (Choi et al, 2010; Lee et al, 2011). However, in most cases, interactome approaches using multiple baits are performed in a given biological context. Moreover, they cannot easily resolve the distribution of a protein of interest among the different complexes in which it might be embedded (Zaki & Mora, 2014). System-wide studies of the composition and dynamics of protein complexes have recently been addressed using an alternative method to AP-MS, protein correlation profiling associated with mass spectrometry (PCP-MS). This approach involves biochemical fractionation procedures and allows the assignment of proteins to specific organelles (Andersen et al, 2003; Foster et al, 2006; Gatto et al, 2010) or, more recently, to protein complexes (Kristensen et al, 2012), by comparing the proteins elution profiles acquired by quantitative MS. As far as interactome studies are concerned, PCP-MS increases the analysis throughput of protein complexes dynamics (Kristensen et al, 2012) and also helps monitoring the impact of subunit isoforms or post-translational modifications in multiprotein complexes (Kirkwood et al, 2013). Interestingly, the heterogeneity of proteasomes can, at least in part, be resolved using PCP-MS on size exclusion chromatography (SEC)-separated protein complexes because this approach is able to distinguish and quantify the relative proportions of singly and doubly capped 20S CPs (Kristensen et al, 2012). The aim of the present study was to decipher proteasome heterogeneity through modern label-free quantitative proteomics. Using PCP-MS on glycerol gradient-separated proteasome complexes, we could first reveal a previously unreported preferential association of immunoproteasome (iP20S) with the PA28αβ RP. Then through the development of a new workflow combining PCP-MS and AP-MS, we could increase the sensitivity of detection of proteasome regulators and thus go deeper into proteasome characterization. Indeed, by correlating proteins abundances across a large set of 24 proteasome samples immunopurified from nine different human cell lines, we observed that the two main 20S proteasome subtypes, sP20S and iP20S, interact with a different subset of regulators. Some of these preferential interactions were validated by artificially or physiologically changing the proportions of both 20S CP subtypes in assembled proteasomes. This novel integrated proteomic workflow provides a valuable tool to better understand the dynamic and complex nature of molecular systems. Results PCP-MS analysis of glycerol density gradient-separated proteasome complexes In a first attempt to resolve proteasome complexes heterogeneity and identify components of the different proteasome subtypes, we performed a PCP-MS analysis on U937 AML cell proteins separated by glycerol density gradient ultracentrifugation (Fig 1A). This cell line is particularly well suited for the analysis of proteasome diversity because it contains equal amounts of each 20S proteasome subtype and, in particular, very similar quantities of β5 and β2i catalytic subunits (Fabre et al, 2013), which are uniquely found in sP20S and iP20S complexes, respectively (Guillaume et al, 2010). To maintain proteasome integrity throughout the purification process, cells were cross-linked in vivo with formaldehyde. After cell lysis, low-MW proteins (below 100 kDa) were discarded by ultrafiltration so that a high-quality separation of high-MW protein complexes could be performed on a glycerol density gradient. Proteins participating in the different complexes resolved in each fraction of the density gradient were then identified and quantified using high-resolution mass spectrometry analysis coupled online to liquid chromatography. Label-free quantification based on peptide ion extracted chromatograms was performed (Mouton-Barbosa et al, 2010; Gautier et al, 2012) using the TOP3 quantification method (Silva et al, 2006). A protein abundance index (PAI) was calculated to approximate the relative quantity of each proteasome subunit and proteasome-associated protein. To handle the inter-run signal variations, heavy internal standards, composed of eight peptides containing isotopically labeled arginine or lysine and eluting all along the chromatographic gradient, were added into each sample before injection. A MS-based intensity profile could therefore be obtained for the 3,353 proteins identified and quantified in the 19 fractions of two biological replicates (Supplementary Table S1). The profiles obtained for the 16 identified subunits of the 19S regulator (Rpt1–6, Rpn1–3, Rpn5, 7–9, 11–13) (Fig 1B, left panel) and for the 11 different non-catalytic subunits of the 20S proteasome (α1–α7, β3, β4, β6, and β7) (Fig 1C, left panel) showed a low dispersion from their respective median profiles, validating the method. Interestingly, the two median profiles of all subunits corresponding to the 20S CP and the 19S RP were somewhat different, in particular in low density fractions 13–16. The high signal detected for the 20S subunits in this profile area corresponds to free 20S core particle elution fractions (no 19S subunits detected) and confirms our previous results showing that a large proportion of 20S proteasome is present as a free particle in the U937 cell line (Fabre et al, 2013). To screen for possible protein interaction among the same complexes, we calculated a relative Euclidian distance, defined in the experimental section and called χ2, between a reference profile and the profile of all the proteins identified in the gradient. This statistical method has proven its efficiency for comparison of sedimentation protein profiles in quantitative proteomic experiments (Andersen et al, 2003; Wiese et al, 2007). The distances obtained from two independent gradient experiments performed on two biological replicates were then plotted to further increase the confidence in protein complexes assignments. When the 19S median profile was taken as reference, all subunits from this protein complex exhibited low χ2 values, under 0.05, and were accordingly gathered in an area very close to the origin of the graph (Fig 1B, middle and right panels). Usp14, a known 19S interacting deubiquitinating enzyme, was also observed at a very close distance (mean χ2 = 0.09), as expected (Fig 1B, right panel). We then applied the PCP-MS analysis to 20S proteasome subunits and observed that the 20S subunits distribution could be clearly distinguished from the other proteins identified in the gradient fractions (Fig 1C, 3rd panel). Unexpectedly, the 20S immunocatalytic β2i subunit was the only 20S subunit observed at a very high distance from the other 20S subunits (mean χ2 = 3.5) (Fig 1C, 2nd panel). However, a close correlation was evidenced with both the PA28α and PA28β subunits of the PA28αβ complex, when the latter was taken as reference profile (Fig 2A). The β2i immunocatalytic subunit is by far the protein showing the closest profile to that of PA28αβ, when compared to all the proteins quantified in the U937 glycerol sedimented lysate (χ2 mean value of 0.025) (Fig 2B and C). As β2i is exclusively found in the iP20S (Guillaume et al, 2010), these data therefore suggest a so far unknown preferential association between the PA28αβ regulator and the iP20S. Figure 1. Protein correlation profiling (PCP) analysis of glycerol density gradient-separated proteasome complexes PCP-MS strategy to identify proteins interacting with specific proteasome subtypes. U937 cells were cross-linked with formaldehyde and lysed, and proteins were concentrated and ultrafiltrated on a 100 kDa cutoff device. Protein complexes were then separated on a 15–40% glycerol gradient. Each fraction of the gradient was analyzed by nano-LC-MS/MS. Protein quantification was performed using the mean XIC of the three most intense validated peptides for each protein, after internal standard calibration using a mix of 8 isotopically labeled peptides. The PCP analysis was performed as described in the 4 section. PCP analysis of the 19S regulatory complex. Protein abundance profiles of 16 proteins of the 19S RP (Rpt1–6, Rpn1–3, Rpn5, 7–9, 11–13, gray lanes) and of their median abundance (black lane) (left panel). PCP analysis is performed by plotting the χ2 values (representing the Euclidian distance between the abundance profile of each protein and the reference profile) of the experimental replicate 2 as a function of the χ2 values of the experimental replicate 1 (middle left panel). The median profile of the 19S complex subunits was used as the reference profile for the calculation of the χ2 values. Different zooms of the graph are represented (middle right and right panels). Light gray dots represent the proteins quantified in all the fractions of the density gradient and blue dots represent 19S subunits (right panel). PCP analysis of proteasome 20S complex. Protein abundance profiles of 17 proteins of the 20S CP (α1–α7, β1–β7, β1i, β2i, β5i, gray lanes) and of their median abundance (black lane) (left panel). PCP analysis is performed by plotting the χ2 values of the experimental replicate 2 as a function of the χ2 values of the experimental replicate 1 (middle left panel). The median profile of the 20S complex subunits was used as the reference profile for the calculation of the χ2 values. Different zooms of the graph are represented (middle right and right panels). Light gray dots represent the proteins quantified in all the fractions of the density gradient and red dots represent 20S subunits. Download figure Download PowerPoint Figure 2. Protein correlation profiling (PCP) analysis using the median profile of the PA28αβ regulator as the reference profile Profiles of the PA28α, PA28β, and the β2i proteins (blue, red and green lines, respectively). Plot of the χ2 values of the experimental replicate 2 as a function of the χ2 values of the experimental replicate 1. A zoom of the graph in (B) is represented and χ2 coordinates for PA28α, PA28β, and β2i proteins are highlighted as blue, red, and green dots, respectively. Light gray dots represent the χ2 coordinates of the proteins quantified in all the fractions of the gradient. The median profile of the PA28α and PA28β subunits was used as the reference profile for the calculation of the χ2 values. Download figure Download PowerPoint The abundances of core subunits of proteasome sub-complexes strongly correlate in nine different human cell lines To confirm the preferential association between the PA28αβ regulator and the iP20S and to allow a deeper characterization of proteasome complexes, proteasomes were affinity-purified from nine different formaldehyde cross-linked cell lines and analyzed by nano-LC-MS/MS, as previously described (Fabre et al, 2013). Formaldehyde cross-linking was shown to be required to stabilize the association of all regulatory particles with the 20S core particle (Fabre et al, 2013). To reach the largest diversity of proteasome complexes, we analyzed the proteolytic complex in a wide variety of human cell lines, including hematopoietic and epithelial cell lines of different origins and exhibiting high variations both in the composition of catalytic subunits and in the stoichiometry of bound regulators or other associated proteins. Very high 20S proteasome purification yields (87 ± 5%) could be obtained (Supplementary Fig S1A). A protein abundance index (PAI) was calculated for each proteasome subunit or proteasome-associated protein identified in the immunopurified complexes obtained from two or three biological replicates of each cell line (Fig 3A and 4 section for details). Importantly, no averaging of biological replicates was performed, to keep the experimental variability. The abundances thus obtained for each protein were then compared pairwise with the ones of another protein, called reference protein, across the 24 proteasome immunoprecipitates (Fig 3A). The correlation between the abundances of two pairwise proteins was estimated using the coefficient of determination (R2), which is more stringent than the usually used Pearson's correlation coefficient (R) (Kirkwood et al, 2013). Interestingly, when using the PAI as a relative abundance metric, very strong correlations were obtained between subunits belonging to the same complex, such as the 20S proteasome non-catalytic subunits α6 and α7 (R2 = 0.98), the 19S regulator subunits Rpn1 and Rpn3 (R2 = 0.96) or the PA28α and PA28β subunits (R2 = 0.96) that compose the PA28αβ activator (Fig 3B–D). All 20S proteasome non-catalytic subunits (α1–α7, β3, β4, β6, and β7) (gathered in a group of proteins called ‘ncP20S’) or subunits belonging to the 19S regulator (Rpt1–6, Rpn1–3, 5–14) were pairwise compared and very high coefficients of determination (0.90 ± 0.07 and 0.93 ± 0.04, respectively) were obtained (Supplementary Fig S1B), demonstrating the efficiency of the AP-MS strategy used to correlate proteins belonging to the same complex. Moreover, the cellular expression levels of many 19S subunits and of some 20S subunits are not correlated (Supplementary Fig S1D), contrary to the abundances of these proteins in immunopurified proteasomes (Supplementary Fig S1B), showing that the immuno-enrichment step is required to highlight subunits interactions within a protein complex. Figure 3. Protein abundance correlation of affinity-purified complexes analyzed by mass spectrometry strategy applied to proteasome complexes A. Proteasome complexes were immunopurified from nine formaldehyde-crosslinked human cell lines and analyzed by nano-LC-MS/MS. Protein abundance indexes (PAIs) were used to represent the abundance of proteins in purified proteasome samples. The correlation between two different proteins was quantified using coefficients of determination (R2). B–E. Correlations of abundances of α7 and α6 (B), Rpn3 and Rpn1 (C), PA28β and PA28α (D), and Rpn3 and PA28β (E). Download figure Download PowerPoint Conversely, when plotting the PAIs of Rpn3 and PA28β, proteins belonging to the 19S RP and the PA28αβ regulator, respectively, a much weaker correlation (R2 = 0.44) could be observed (Supplementary Fig S3). This is probably because the 19S RP is involved in several different types of functional proteasome complexes (30S, 26S for instance) in addition to hybrid proteasome (one 19S RP and one PA28 RP associated with one 20S CP), which could indeed be observed in U937 cells when immunopurifying PA28β (Supplementary Fig S1C). These results therefore show that the protein abundance index, associated with the R2, is able to quantitatively describe the correlation of the relative abundances of proteins constituting core subunits of proteasome sub-complexes purified from a large set of human cell lines exhibiting a high variety of proteasome complexes. Comparing the abundances of the different proteasome subunits and associated proteins in purified proteasome preparations therefore appears as an efficient approach to unravel putative binary protein interactions among proteasome complexes. Proteasome subunits and associated proteins cluster differently on the basis of their abundances across the nine cell lines To investigate further the composition of proteasome complexes and highlight putative unknown interactions among specific proteasome subunits or associated proteins, we pairwise compared, across the biological replicates of the nine cell lines, the abundances of all the identified proteins in the immunoprecipitates with the abundances of eight important proteasome sub-complexes or regulators, that we called ‘references’. These were the 19S, PA28αβ, PA28γ, and PA200 activators, the PI31 proteasome regulator, the two major 20S proteasome subtypes, the sP20S (represented by the β5 catalytic subunit) and the iP20S (represented by the β2i catalytic subunit; Guillaume et al, 2010), and the ncP20S gathering the 20S non-catalytic subunits (thus representing the total 20S proteasome). The abundances of these different complexes were obtained by calculating the median PAI of their different subunits, as detailed in the 4 section. Of the 170 human proteasome-interacting proteins identified in previous AP-MS experiments (Wang & Huang, 2008; Andersen et al, 2009; Bousquet-Dubouch et al, 2009), 120 have been quantified in this survey and, among these, 70 proteins exhibited a high correlation (R > 0.8) with at least one of the references, suggesting" @default.
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• W2169107124 title "Deciphering preferential interactions within supramolecular protein complexes: the proteasome case" @default.
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• W2169107124 doi "https://doi.org/10.15252/msb.20145497" @default.
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