The MHC I immunopeptidome conveys to the cell surface an integrative view of cellular regulation
2011; Springer Nature; Volume: 7; Issue: 1 Linguagem: Inglês
10.1038/msb.2011.68
ISSN1744-4292
AutoresÉtienne Caron, Krystel Vincent, Marie‐Hélène Fortier, Jean‐Philippe Laverdure, Alexandre Bramoullé, Marie‐Pierre Hardy, Grégory Voisin, Philippe P. Roux, Sébastien Lemieux, Pierre Thibault, Claude Perreault,
Tópico(s)Glycosylation and Glycoproteins Research
ResumoArticle27 September 2011Open Access The MHC I immunopeptidome conveys to the cell surface an integrative view of cellular regulation Etienne Caron Etienne Caron Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, Quebec, Canada Department of Medicine, Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada Search for more papers by this author Krystel Vincent Krystel Vincent Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, Quebec, Canada Search for more papers by this author Marie-Hélène Fortier Marie-Hélène Fortier Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, Quebec, Canada Department of Chemistry, Université de Montréal, Montreal, Quebec, Canada Search for more papers by this author Jean-Philippe Laverdure Jean-Philippe Laverdure Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, Quebec, Canada Search for more papers by this author Alexandre Bramoullé Alexandre Bramoullé Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, Quebec, Canada Search for more papers by this author Marie-Pierre Hardy Marie-Pierre Hardy Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, Quebec, Canada Search for more papers by this author Grégory Voisin Grégory Voisin Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, Quebec, Canada Search for more papers by this author Philippe P Roux Philippe P Roux Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, Quebec, Canada Department of Pathology and Cell Biology, Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada Search for more papers by this author Sébastien Lemieux Sébastien Lemieux Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, Quebec, Canada Department of Computer Science and Operations Research, Faculty of Arts and Sciences, Université de Montréal, Montreal, Quebec, Canada Search for more papers by this author Pierre Thibault Corresponding Author Pierre Thibault Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, Quebec, Canada Department of Chemistry, Université de Montréal, Montreal, Quebec, Canada Search for more papers by this author Claude Perreault Corresponding Author Claude Perreault Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, Quebec, Canada Department of Medicine, Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada Search for more papers by this author Etienne Caron Etienne Caron Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, Quebec, Canada Department of Medicine, Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada Search for more papers by this author Krystel Vincent Krystel Vincent Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, Quebec, Canada Search for more papers by this author Marie-Hélène Fortier Marie-Hélène Fortier Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, Quebec, Canada Department of Chemistry, Université de Montréal, Montreal, Quebec, Canada Search for more papers by this author Jean-Philippe Laverdure Jean-Philippe Laverdure Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, Quebec, Canada Search for more papers by this author Alexandre Bramoullé Alexandre Bramoullé Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, Quebec, Canada Search for more papers by this author Marie-Pierre Hardy Marie-Pierre Hardy Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, Quebec, Canada Search for more papers by this author Grégory Voisin Grégory Voisin Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, Quebec, Canada Search for more papers by this author Philippe P Roux Philippe P Roux Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, Quebec, Canada Department of Pathology and Cell Biology, Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada Search for more papers by this author Sébastien Lemieux Sébastien Lemieux Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, Quebec, Canada Department of Computer Science and Operations Research, Faculty of Arts and Sciences, Université de Montréal, Montreal, Quebec, Canada Search for more papers by this author Pierre Thibault Corresponding Author Pierre Thibault Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, Quebec, Canada Department of Chemistry, Université de Montréal, Montreal, Quebec, Canada Search for more papers by this author Claude Perreault Corresponding Author Claude Perreault Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, Quebec, Canada Department of Medicine, Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada Search for more papers by this author Author Information Etienne Caron1,2,‡, Krystel Vincent1,‡, Marie-Hélène Fortier1,3,‡, Jean-Philippe Laverdure1, Alexandre Bramoullé1, Marie-Pierre Hardy1, Grégory Voisin1, Philippe P Roux1,4, Sébastien Lemieux1,5, Pierre Thibault 1,3 and Claude Perreault 1,2 1Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, Montreal, Quebec, Canada 2Department of Medicine, Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada 3Department of Chemistry, Université de Montréal, Montreal, Quebec, Canada 4Department of Pathology and Cell Biology, Faculty of Medicine, Université de Montréal, Montreal, Quebec, Canada 5Department of Computer Science and Operations Research, Faculty of Arts and Sciences, Université de Montréal, Montreal, Quebec, Canada ‡These authors contributed equally to this work. *Corresponding authors. Institute for Research in Immunology and Cancer (IRIC), Université de Montréal, PO Box 6128 Station Centre-Ville, Montreal, Quebec, Canada H3C 3J7. Tel.: +1 514 343 6910; Fax: +1 514 343 6843; E-mail: [email protected] or Tel.: +1 514 343 6126; Fax: +1 514 343 5839; E-mail: [email protected] Molecular Systems Biology (2011)7:533https://doi.org/10.1038/msb.2011.68 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 Self/non-self discrimination is a fundamental requirement of life. Endogenous peptides presented by major histocompatibility complex class I (MHC I) molecules represent the essence of self for CD8 T lymphocytes. These MHC I peptides (MIPs) are collectively referred to as the immunopeptidome. From a systems-level perspective, very little is known about the origin, composition and plasticity of the immunopeptidome. Here, we show that the immunopeptidome, and therefore the nature of the immune self, is plastic and moulded by cellular metabolic activity. By using a quantitative high-throughput mass spectrometry-based approach, we found that altering cellular metabolism via the inhibition of the mammalian target of rapamycin results in dynamic changes in the cell surface MIPs landscape. Moreover, we provide systems-level evidence that the immunopeptidome projects at the cell surface a representation of biochemical networks and metabolic events regulated at multiple levels inside the cell. Our findings open up new perspectives in systems immunology and predictive biology. Indeed, predicting variations in the immunopeptidome in response to cell-intrinsic and -extrinsic factors could be relevant to the rational design of immunotherapeutic interventions. Synopsis Quantitative mass spectrometry reveals changes in the peptides presented by major histocompatibility complex class I molecules when the mTOR pathway is perturbed. These data show that the immunopeptidome is plastic and provides information on the internal state of the cell. Using a quantitative high-throughput mass spectrometry-based approach, we found that inhibition of the mammalian target of rapamycin (mTOR) results in dynamic changes in the composition of the MHC I immunopeptidome. We provide systems-level evidence that the MHC I immunopeptidome projects at the cell surface a representation of biochemical networks and metabolic events regulated at multiple levels (transcriptional and co- or post-translational level) inside the cell. We demonstrate that the composition of the MHC I immunopeptidome changes in response to metabolic perturbations and we provide insights into how mammalian cells communicate their metabolic status to the adaptive immune system. Introduction While unicellular eukaryotes primarily employ self/non-self discrimination to avoid self-mating, multicellular organisms use self/non-self discrimination primarily in immune defense (Boehm, 2006). Failure to respond to non-self can lead to death from infection whereas untoward response to self paves the way to autoimmunity. Peptides presented by major histocompatibility complex class I (MHC I) molecules represent the essence of self for CD8 T lymphocytes (Rammensee et al, 1993; Klein et al, 2009). These MHC I-associated peptides (MIPs) regulate all key events that occur during the lifetime of CD8 T cells in the thymus and the periphery (Goldrath and Bevan, 1999; Klein et al, 2009). MIPs are presented at the surface of most nucleated cells in jawed vertebrates and are collectively referred to as the MHC I immunopeptidome (Istrail et al, 2004). Classic reductionist approaches have established the broad outlines of the MIP processing system and shown that MIPs derive from degradation of endogenous proteins by the proteasome and other peptidases (Yewdell et al, 2003; Hammer et al, 2007). They have also highlighted the complexity of the MIP repertoire. Thus, MHC I molecules present peptides encoded not only in the primary open reading frames but also those encoded in alternate reading frames (Starck and Shastri, 2011). Moreover, progresses in mass spectrometry (MS) have allowed increasingly sophisticated and comprehensive large-scale analyses of MIPs (Mester et al, 2011). Large-scale analyses have yielded unprecedented insights into the peptide specificities and motifs of MHC molecules and the diversity of the MIP repertoire (Hunt et al, 1992; Engelhard et al, 1993; Bonner et al, 2002; Weinzierl et al, 2008). They have also demonstrated that MIPs derive from all cell compartments and that the MIP repertoire can be modified by neoplastic transformation (Schirle et al, 2000; Hickman et al, 2004; Weinzierl et al, 2007; Fortier et al, 2008). Nevertheless, we still know very little about the genesis and molecular composition of the immunopeptidome: why do proteins such as STT3B yield abundant MIPs while others do not (Perreault, 2010)? MS studies have also revealed that the immunopeptidome is not a random sample of the proteome: many abundant proteins do not generate MIPs, while some low-abundance proteins generate large amounts of MIPs (Milner et al, 2006). Furthermore, large-scale analyses have yielded conflicting results on the relation between the transcriptome and the MIP repertoire (Weinzierl et al, 2007; Fortier et al, 2008; Mester et al, 2011). Therefore, further systematic studies based on high-throughput technologies and integrative approaches are needed in order to elucidate the mechanisms that mould the immunopeptidome. In-depth mechanistic understanding of the immunopeptidome biogenesis would allow prediction of its molecular composition and would therefore be highly relevant to the development of immunotherapies (Zarling et al, 2006; Sette and Rappuoli, 2010). A key unresolved question is whether the immunopeptidome of a particular cell is plastic and affected by its metabolic state. This issue is of fundamental importance because cells targeted by CD8 T lymphocytes (i.e. infected and neoplastic cells) are metabolically perturbed by intracellular parasite/viruses or various transforming events. We addressed this question by studying the immunopeptidome of a mouse lymphoma cell line (EL4) treated with the classic mammalian target of rapamycin (mTOR) inhibitor, rapamycin. We selected this model because rapamycin is an exquisitely specific inhibitor of the serine/threonine kinase mTOR, and because mTOR is a central regulator of cellular homeostasis through its involvement in promoting anabolic and inhibiting catabolic processes (Sengupta et al, 2010). Furthermore, mTOR signaling is regulated in numerous physiological and pathological conditions in all cell types, including neoplastic and immune cells (Araki et al, 2009; Caron et al, 2010; Sengupta et al, 2010). Results The effect of rapamycin on mTOR signaling in EL4 cells As a prelude to our peptidomic studies, we first assessed the effects of rapamycin on EL4 cells. The first mTOR complex, mTORC1, phosphorylates both S6K1 and 4E-BP1 (Dowling et al, 2010). Recent studies have shown that rapamycin, previously thought to completely inhibit mTORC1 activity, differentially affects 4E-BP1 and S6K1 (Choo et al, 2008; Choo and Blenis, 2009; Thoreen et al, 2009). While rapamycin treatment completely and sustainably inhibits S6K1 activation, it partly and variably inhibits 4E-BP1 phosphorylation (Choo et al, 2008). To evaluate how rapamycin affected mTORC1 signaling in EL4 cells, we treated EL4 cells with rapamycin for different time durations up to 48 h. As expected, Thr-389 on S6K1 was fully dephosphorylated upon rapamycin treatment, correlating with a decrease in cell size (Figure 1A and B). In contrast, Ser-65 and Thr-37/46 on 4E-BP1 were partially dephosphorylated by rapamycin (Figure 1A). As previously reported, we also noted that abundance of 4E-BP1 was decreased after 24 h of rapamycin treatment (Figure 1A) (Dilling et al, 2002). Because 4E-BP1 is critically involved in cap-dependent translation via regulation of eIF4E, these results suggested that translation was maintained in EL4 cells in the presence of rapamycin. Accordingly, we observed that protein synthesis decreased during the first 12 h of rapamycin treatment, but progressively recovered thereafter (Figure 1C). Prolonged rapamycin treatment for up to 24 h has also been shown to inhibit the assembly of mTORC2, the second mTOR complex (Sarbassov et al, 2006). Here, we observed that mTORC2-mediated phosphorylation of AKT at Ser-473 was transiently decreased but recovered and was increased after treatment for 48 h. Thus, rapamycin treatment for 48 h inhibited mTORC1, but activated mTORC2 in EL4 cells. Collectively, these results showed that rapamycin-mediated mTORC1 inhibition perturbed protein synthesis and cell size by differentially inhibiting S6K1 and 4E-BP1 in EL4 cells (Figure 1D). Of direct relevance to our analyses of the MIP landscape, rapamycin treatment did not affect expression levels of key proteins involved in the MHC I antigen processing and presentation pathway (Supplementary Figure S1). Figure 1.Rapamycin differentially inhibits S6K1 versus 4E-BP1 in EL4 cells. Cells were treated with 20 ng/ml of rapamycin for the indicated time periods. (A) Levels of the indicated proteins were determined by western blotting. β-Actin served as a loading control. Data are representative of three independent experiments. (B) EL4 cells were treated or not (ctrl) with rapamycin for 48 h. One thousand cells were counted for each condition. Cell size was measured by light microscopy. The red line corresponds to the average cell size. Data are representative of three independent experiments. *P<0.0005 (Student's t-test). (C) Relative protein synthesis was measured by [3H]-leucine incorporation. Data (mean±s.d.) are representative of three independent experiments. *P<0.01, **P 2.0; P 2.5; P 2.0 (P 2.5; P<0.05; fold difference and P-value based on Fortier et al, 2008) are highlighted in the blue boxes. (C, D) GO enrichment analyses were performed for DEM source genes and DEGs. In all, 38 DEM source genes (blue) were associated to 6 significantly enriched cellular processes. In all, 101 (green) and 70 (red) genes coding for under- and overexpressed mRNAs were associated to 7 and 8 significantly enriched cellular processes, respectively. (C) Venn diagram showing no overlap between DEM source genes and DEGs. Dashed boxes show representative genes that contributed to enrichment of four cellular processes in both DEM source genes and DEGs. (D) Venn diagram showing functional overlap between cellular processes overrepresented in DEM source genes and DEGs. The four cellular processes overrepresented in both DEM source genes and DEGs are listed in the dashed boxes. Download figure Download PowerPoint Analysis of Gene Ontology (GO) annotations revealed that 101 and 70 genes (Figure 3C; Supplementary Figure S3B and C) coding for under- and overexpressed mRNAs were associated to 7 and 8 (Figure 3D) significantly enriched cellular processes, respectively (P<0.05) (Supplementary Table S3). GO term analysis on the 98 DEM source genes also revealed a significant (P<0.05) enrichment for 6 cellular processes (Figure 3D) implicating 38 DEM source genes (Figure 3C; Supplementary Figure S3A; Supplementary Table S3). Analysis of DEGs and DEM source genes involved in enriched cellular processes yielded several findings. First, there was no overlap between the 171 DEGs and the 38 DEM source genes (Figure 3C). Second, cross-comparison of the overrepresented functional groups identified four distinct cellular processes (protein transport, cell cycle/proliferation, DNA replication and transcription) that were enriched in both DEGs and DEM source genes (Figure 3D). In order to determine whether this result implies a significant functional relationship between transcriptome and immunopeptidome variations, we developed an all-pairs-shortest-path matrix, which scores the functional connectivity between two lists of genes (Figure 4A). Using this matrix, we first calculated a connectivity score between the 171 DEGs and the 38 DEM source genes identified above. Then, bootstrapping was used as a statistical sampling method to calculate control connectivity scores from 105 sets of 38 randomly selected MIP source genes from a database of 891 unique source genes encoding H2b-associated peptides (Supplementary Table S4). These analyses revealed that the 38 DEM source genes were tightly interconnected to the 171 DEGs (bootstrapping; P=0.004) (Figure 4B). Hence, while most DEMs did not originate directly from DEGs, they do originate from genes that are tightly functionally connected to DEGs. In other words, this systems-level analysis demonstrates that rapamycin-mediated mTOR inhibition induces functionally coherent changes in the transcriptome and the immunopeptidome. Given the tremendous complexity of the transcriptome, this finding suggests that the immunopeptidome of the cell is more complex than anticipated and that its plasticity might be extensive. Figure 4.DEM source genes are tightly connected to transcriptomic changes and the mTOR network. (A) An all-pairs-shortest-path matrix was developed by using computed scores (S) in the STRING database (http://string-db.org/). The all-pairs-shortest-path matrix was used to calculate functional associations between (1) DEM source genes and DEGs and (2) DEM source genes and mTOR network components. Each functional association in the all-pairs-shortest-path matrix was transformed into a distance (D). The matrix shows DEM source genes (rows), DEGs or mTOR network components (columns), and the shortest path distance between every pair of nodes (genes/proteins) in the association network (e.g. Dw-z and Dx-y). A connectivity score corresponds to the mean of the shortest path distance between every pair of nodes in a given matrix. (B, C) The all-pairs-shortest-path matrix was used to calculate functional connectivity scores. The red lines represent the connectivity score between DEM source genes and DEGs (B), and between DEM source genes and mTOR network components (C). A bootstrap procedure was used to calculate control connectivity scores represented by the Gaussian distributions. Download figure Download PowerPoint DEMs arise from biochemical networks connected to mTOR The above results suggested that DEMs originated from genes connected to the mTOR network. However, considering that mTOR has a pervasive role in protein synthesis and degradation (Caron et al, 2010), we could not discard the possibility that DEMs originated from some non-specific generic effect of rapamycin on protein metabolism. In the latter case, DEM-coding genes would not be tightly connected to the mTOR network. Hence, in order to further evaluate the relationship between DEMs and mTOR, we first conducted an analysis on the 98 DEM source genes (Figure 3B; Supplementary Table S2) using the interaction network database STITCH (Kuhn et al, 2010). This analysis uncovered a network containing 30 DEM source genes that were interconnected and organized within discrete functional modules (Supplementary Figure S4). Strikingly, the network included the chemicals rapamycin and everolimus (rapamycin analog) in addition to components (e.g. Rictor, Sgk1) and modules (e.g. mTOR signaling, translation, lipid biosynthesis) known to be directly regulated by mTOR (Caron et al, 2010). Therefore, we reasoned that rapamycin-mediated changes in the immunopeptidome might originate from genes that are very closely connected to components of the mTOR signaling network. To systematically evaluate this assumption, we measured the connectivity score between the 30 DEM source genes and components extracted from a comprehensive map of the mTOR interactome and signaling network (Supplementary Table S5 based on Caron et al, 2010). By using the all-pair-shortest-path matrix described above, we calculated that the 30 DEM source genes were strongly interconnected to the mTOR network components relative to random assignments (bootstrapping; P<10−5) (Figure 4C). This systems-level analysis demonstrates that a substantial fraction of rapamycin-induced variations in the immunopeptidome arise from biochemical networks whose components are highly connected to the target of rapamycin (i.e. mTOR). Thus, our data reinforce the notion that the immunopeptidome projects a functional representation of intracellular metabolic changes to the cell surface. Further immunopeptidomic studies are needed to evaluate whether the mTOR network regulates the repertoire of MIPs in other contexts. To visualize the organization of relationships between DEM source genes, the transcriptome and the mTOR network, we integrated in a global network the mTOR interactome and signaling network (Supplementary Table S5 based on Caron et al, 2010), the 98 DEM source genes (Figure 3B; Supplementary Table S2) and the 171 DEGs (Figure 3C; Supplementary Figure S3B and C). We found that 33 DEM source genes were constituents of 7 discrete functional subnetworks (Figure 5A). All modules included components of the mTOR network. Integration of microarray data revealed that DEGs were found in 5 out of the 7 modules containing DEM source genes. Notably, two modules contained DEM source genes but no DEGs: the proteasome and the core mTOR signaling modules. Overall, 82% of the DEM source
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