ERK7 is a negative regulator of protein secretion in response to amino-acid starvation by modulating Sec16 membrane association
2011; Springer Nature; Volume: 30; Issue: 18 Linguagem: Inglês
10.1038/emboj.2011.253
ISSN1460-2075
AutoresMargarita Zacharogianni, Vangelis Kondylis, Yang Tang, Hesso Farhan, Despina Xanthakis, Florian Fuchs, Michael Boutros, Cathérine Rabouille,
Tópico(s)Protein Kinase Regulation and GTPase Signaling
ResumoArticle16 August 2011free access ERK7 is a negative regulator of protein secretion in response to amino-acid starvation by modulating Sec16 membrane association Margarita Zacharogianni Margarita Zacharogianni Department of Cell Biology, Cell microscopy Centre, UMC Utrecht, Heidelberglaan, Utrecht, The Netherlands Hubrecht Institute for Stem Cell and Developmental Biology, Utrecht, The Netherlands Search for more papers by this author Vangelis Kondylis Vangelis Kondylis Department of Cell Biology, Cell microscopy Centre, UMC Utrecht, Heidelberglaan, Utrecht, The NetherlandsPresent address: Mouse Genetics and Inflammation Laboratory, Institute for Genetics, University of Cologne, Zulpicher Str. 47a, 50674 Cologne, Germany. Search for more papers by this author Yang Tang Yang Tang Department of Cell Biology, Cell microscopy Centre, UMC Utrecht, Heidelberglaan, Utrecht, The NetherlandsPresent address: Tianjin Institute of Urological Surgery, The Second Affiliated Hospital of Tianjin Medical University, Tianjin, China. Search for more papers by this author Hesso Farhan Hesso Farhan Biotechnology Institute Thurgau Unterseestrasse, Kreuzlingen, Switzerland Department of Biology, University of Konstanz, Konstanz, Germany Search for more papers by this author Despina Xanthakis Despina Xanthakis Department of Cell Biology, Cell microscopy Centre, UMC Utrecht, Heidelberglaan, Utrecht, The Netherlands Hubrecht Institute for Stem Cell and Developmental Biology, Utrecht, The Netherlands Search for more papers by this author Florian Fuchs Florian Fuchs German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics and University of Heidelberg, Cell and Molecular Biology, Medical Faculty Mannheim, Division Signaling and Functional Genomics, Im Neuenheimer Feld, Heidelberg, Germany Search for more papers by this author Michael Boutros Michael Boutros German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics and University of Heidelberg, Cell and Molecular Biology, Medical Faculty Mannheim, Division Signaling and Functional Genomics, Im Neuenheimer Feld, Heidelberg, Germany Search for more papers by this author Catherine Rabouille Corresponding Author Catherine Rabouille Department of Cell Biology, Cell microscopy Centre, UMC Utrecht, Heidelberglaan, Utrecht, The Netherlands Hubrecht Institute for Stem Cell and Developmental Biology, Utrecht, The Netherlands Search for more papers by this author Margarita Zacharogianni Margarita Zacharogianni Department of Cell Biology, Cell microscopy Centre, UMC Utrecht, Heidelberglaan, Utrecht, The Netherlands Hubrecht Institute for Stem Cell and Developmental Biology, Utrecht, The Netherlands Search for more papers by this author Vangelis Kondylis Vangelis Kondylis Department of Cell Biology, Cell microscopy Centre, UMC Utrecht, Heidelberglaan, Utrecht, The NetherlandsPresent address: Mouse Genetics and Inflammation Laboratory, Institute for Genetics, University of Cologne, Zulpicher Str. 47a, 50674 Cologne, Germany. Search for more papers by this author Yang Tang Yang Tang Department of Cell Biology, Cell microscopy Centre, UMC Utrecht, Heidelberglaan, Utrecht, The NetherlandsPresent address: Tianjin Institute of Urological Surgery, The Second Affiliated Hospital of Tianjin Medical University, Tianjin, China. Search for more papers by this author Hesso Farhan Hesso Farhan Biotechnology Institute Thurgau Unterseestrasse, Kreuzlingen, Switzerland Department of Biology, University of Konstanz, Konstanz, Germany Search for more papers by this author Despina Xanthakis Despina Xanthakis Department of Cell Biology, Cell microscopy Centre, UMC Utrecht, Heidelberglaan, Utrecht, The Netherlands Hubrecht Institute for Stem Cell and Developmental Biology, Utrecht, The Netherlands Search for more papers by this author Florian Fuchs Florian Fuchs German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics and University of Heidelberg, Cell and Molecular Biology, Medical Faculty Mannheim, Division Signaling and Functional Genomics, Im Neuenheimer Feld, Heidelberg, Germany Search for more papers by this author Michael Boutros Michael Boutros German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics and University of Heidelberg, Cell and Molecular Biology, Medical Faculty Mannheim, Division Signaling and Functional Genomics, Im Neuenheimer Feld, Heidelberg, Germany Search for more papers by this author Catherine Rabouille Corresponding Author Catherine Rabouille Department of Cell Biology, Cell microscopy Centre, UMC Utrecht, Heidelberglaan, Utrecht, The Netherlands Hubrecht Institute for Stem Cell and Developmental Biology, Utrecht, The Netherlands Search for more papers by this author Author Information Margarita Zacharogianni1,2,‡, Vangelis Kondylis1,‡, Yang Tang1,‡, Hesso Farhan3,4, Despina Xanthakis1,2, Florian Fuchs5, Michael Boutros5 and Catherine Rabouille 1,2 1Department of Cell Biology, Cell microscopy Centre, UMC Utrecht, Heidelberglaan, Utrecht, The Netherlands 2Hubrecht Institute for Stem Cell and Developmental Biology, Utrecht, The Netherlands 3Biotechnology Institute Thurgau Unterseestrasse, Kreuzlingen, Switzerland 4Department of Biology, University of Konstanz, Konstanz, Germany 5German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics and University of Heidelberg, Cell and Molecular Biology, Medical Faculty Mannheim, Division Signaling and Functional Genomics, Im Neuenheimer Feld, Heidelberg, Germany ‡These authors contributed equally to this work *Corresponding author. Hubrecht Institute for Stem Cell and Developmental Biology, Uppsalalaan 8, Utrecht 3584 CT, Netherlands. Tel.: +31 30 212 1941; Fax.: +31 30 251 6464; E-mail: [email protected] The EMBO Journal (2011)30:3684-3700https://doi.org/10.1038/emboj.2011.253 Present address: Mouse Genetics and Inflammation Laboratory, Institute for Genetics, University of Cologne, Zulpicher Str. 47a, 50674 Cologne, Germany. Present address: Tianjin Institute of Urological Surgery, The Second Affiliated Hospital of Tianjin Medical University, Tianjin, China. 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 ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info RNAi screening for kinases regulating the functional organization of the early secretory pathway in Drosophila S2 cells has identified the atypical Mitotic-Associated Protein Kinase (MAPK) Extracellularly regulated kinase 7 (ERK7) as a new modulator. We found that ERK7 negatively regulates secretion in response to serum and amino-acid starvation, in both Drosophila and human cells. Under these conditions, ERK7 turnover through the proteasome is inhibited, and the resulting higher levels of this kinase lead to a modification in a site within the C-terminus of Sec16, a key ER exit site component. This post-translational modification elicits the cytoplasmic dispersion of Sec16 and the consequent disassembly of the ER exit sites, which in turn results in protein secretion inhibition. We found that ER exit site disassembly upon starvation is TOR complex 1 (TORC1) independent, showing that under nutrient stress conditions, cell growth is not only inhibited at the transcriptional and translational levels, but also independently at the level of secretion by inhibiting the membrane flow through the early secretory pathway. These results reveal the existence of new signalling circuits participating in the complex regulation of cell growth. Introduction Secretion takes place through the membrane of the secretory pathway that comprises the rough ER, ER exit sites (ERES or tER sites), where newly synthesized proteins are packaged into budding COPII vesicles, ER-Golgi intermediate compartment, Golgi apparatus and post-Golgi carriers. In Drosophila, tER sites are closely associated with individual pairs of Golgi stacks forming what we, and others have called tER-Golgi units that represent the early secretory pathway (Kondylis and Rabouille, 2009). One key protein required for tER site organization and COPII vesicle budding is the large hydrophilic protein Sec16 that localizes to the ER cup overlaying the clusters of COPII vesicles. Upon its functional disruption by depletion or mutation, tER site biogenesis is impaired and secretion is drastically inhibited (Connerly et al, 2005; Watson et al, 2005; Bhattacharyya and Glick, 2007; Ivan et al, 2008; Hughes et al, 2009). Despite the identification of many components underlying the functional organization of the secretory pathway (Bonifacino and Glick, 2004; Spang, 2009), two recent genome-wide RNAi screens (Bard et al, 2006; Wendler et al, 2010) have led to the discovery of novel proteins required for constitutive secretion, including Tango1 (Bard et al, 2006; Saito et al, 2009), as well as Grysum and Kish (Wendler et al, 2010). However, how secretion is regulated qualitatively and quantitatively in response to changes imposed by cell growth, nutrient availability, stress and differentiation is not completely understood; In particular, the molecular mechanisms through which exogenous stimuli are sensed and relayed to the secretory machinery remains largely unknown. The relationship between signalling and secretion has only recently started to emerge. Kinases have been recently demonstrated to reside on membrane compartments of the early secretory pathway and activate signalling cascades that modify its functional organization (for reviews, see Quatela and Philips, 2006; Omerovic and Prior, 2009; Sallese et al, 2009; Farhan and Rabouille, 2011). For instance, the budding of COPII-coated vesicles is blocked by the kinase inhibitor H89 (Aridor and Balch, 2000), ER export is inhibited by the phosphatase inhibitor okadaic acid (Pryde et al, 1998) and Akt has recently been shown to phosphorylate Sec24 (Sharpe et al, 2010). Furthermore, a siRNA screen depleting 916 human kinases and phosphatases was performed to uncover regulators of the secretory pathway. The Mitotic-Associated Protein Kinase (MAPK) Extracellularly regulated kinase (ERK) 2, which is activated by epidermal growth factor (EGF) through Ras, was shown to directly phosphorylate Sec16 on Threonine 415. This phosphorylation event led to an increase in the ERES number and secretion (Farhan et al, 2010). This reinforced the notion that the early secretory pathway is regulated by environmental conditions and that components of the secretory pathway are direct targets of signalling. Interestingly, the secretory pathway also responds to intracellular stimuli, such as increased cargo load (Guo and Linstedt, 2006; Farhan et al, 2008; Pulvirenti et al, 2008). In order to define conserved kinases regulating the functional organization of the early secretory pathway and identify new ones, we performed a microscopy-based primary RNAi screen in Drosophila S2 cells (Kondylis et al, 2011), which show a high depletion efficiency by RNAi. Furthermore, in comparison with mammalian genomes, the Drosophila genome has less genetic redundancy, facilitating the identification of candidates that might have been missed in human cells. Results Primary screen and candidate validation In the primary screen, we depleted 245 kinases in duplicate (Boutros et al, 2004), in addition to the positive and negative controls (depletion of apoptotic inhibitor DIAP1, Sec16, Abi and Scar) and we scored for the organization of the early secretory pathway marked by Golgi protein Fringe-GFP (as described in Kondylis et al, 2011 and Supplementary data; Figure 1). Depletion of 43 kinases significantly altered the Golgi organization in one or both plates, and the most common phenotype observed was an increase in the number of Fringe-GFP fluorescent spots (Supplementary Table S1). Depletion of 49 proteins exhibited a phenotype discrepancy between the two plates tested, while no data were obtained for another 50 proteins. These kinases together with those whose depletion did not seemingly affect the Golgi organization were not further examined (see Supplementary Materials and Methods). Figure 1.Overview of the microscopy-based RNAi kinase screen. The primary screen was performed in 384-well plates (in duplicate) using Fringe-GFP S2 cells and dsRNAs transcribed from the HFA library targeting 254 genes (245 kinases). The cells were immunolabelled with anti-GFP and anti α-tubulin antibodies as well as Hoechst and were viewed by widefield microscopy. Forty-three candidates (scored in the two plates or only in one) were identified, whereas 112 depletions did not lead to a Golgi phenotype. Fifty depletions led to an unclear phenotype because of a phenotypic discrepancy between the two plates examined (a Golgi phenotype was observed 'in one plate only'). The phenotype of 49 depletions was 'not determined' because the data were not recorded properly (out of focus or lack of cells). The validation screen was performed using different dsRNAs transcribed from a second generation RNAi library (HD2) to target 30 out of 43 candidates. It was performed in Fringe-GFP S2 cells seeded in 24-well plates that were immunolabelled with Sec16/PDI/Dapi and viewed by confocal microscopy. In all, 26 out of 30 candidates were validated. The depletion phenotypes of 11 candidates were characterized (using a third set of independent dsRNAs) and 8 were cloned, localized and overexpressed leading to the identification of ERK7. Download figure Download PowerPoint Out of these 43 candidate regulators of the early secretory pathway organization, we selected 30 that were validated using a different dsRNA after analysis by confocal microscopy (Supplementary Table S1). In all, 26 out of the 30 genes tested were confirmed, illustrating the robustness of our approach (Supplementary Tables S1 and S2). In line with the primary screen, most of the depletions increased the number of tER-Golgi units in a significant percentage of cells (Figure 2A), a phenotype thereafter referred to as MG for 'more and smaller Golgi spots' (Table I). Depending on the penetrance of the phenotype, the candidates were grouped as very strong (MG++++; cdc2), strong (MG+++; CG10738 and CG10177), moderate (MG++; CG32703) and weak (MG+) (Figure 2A; Supplementary Table S2). A decrease in the number of Fringe-GFP dots was more rarely observed (LS for 'less Golgi spots'), likely due to aggregation of tER-Golgi units (Supplementary Tables S1 and S2). This was the case for Wallenda (Wnd, Figure 2A) and CG4041 (Table I). The RNAi depletion efficiency in the secondary screen was tested for four hits using cells transfected with V5-tagged versions of these analysed proteins (see below), and was found to be very efficient (Supplementary Figure S1A). Figure 2.Examples of different phenotypic groups from the confirmation/validation screen. (A) Visualization of tER-Golgi units (Sec16 and Fringe-GFP, respectively) upon different RNAi depletions by confocal microscopy. Typical pattern of tER-Golgi units in mock-treated cells (−dsRNA). The very strong (+ds cdc2), strong (+ds sticky; +ds CG10177) and moderate (+ ds CG32703) MG phenotype (more and smaller Golgi spots) are presented as well as the LS phenotype (less spots, +ds wallenda/wnd). The pictures represent 2D projections of confocal sections. Scale bar: 5 μm. (B) The number of S2 cells after a 5-day incubation with the indicated dsRNAs expressed as percentage relative to the number of mock-treated cells. Red and blue columns indicate genes whose depletion led to a significant decrease or increase in cell proliferation, respectively. Error bars represent s.d. from at least three independent experiments. Conditions with P<0.01, 0.01<P<0.05 and 0.05<P<0.10 are indicated with triple, double and single asterisks, respectively. (C, D) Cell-cycle distribution of live S2 cells after 5 days incubation with the indicated dsRNAs determined by staining their DNA content. The population of G1 (M1), S/G2/M (M2) or sub-G1 (M3) cells in each condition was quantified by FACS analysis. Percentage of gated cells in S/G2/M phase (4N) (normalized to the respective value of mock-treated cells, which was considered as 100%) of one representative experiment (C). Red and blue columns indicate genes whose depletion leads to a significant decrease or increase in the percentage of cells in S/G2/M phase, respectively. cdc25 and myb depletions (n=3) lead to an average of 152.61%±7.85 (P-value of 0.010) and 144.62%±12.41 (P-value of 0.036), respectively. For CG10738 (#1 and #2) depletion (n=3), the average is 71.60%±7.12 with a P-value of 0.014. Representative examples are shown in (D). Note the increase in G1 population upon depletion of CG10738 kinase. (E) Efficiency of anterograde transport of Delta S2 cells incubated for 5 days with the indicated dsRNAs, followed by 1-h induction of Delta with CuSO4 and 75 min chase to allow its transport to plasma membrane. Fixed cells were labelled for Delta and dGMAP (cis-Golgi marker). Scale bars: 5 μm. Download figure Download PowerPoint Table 1. Phenotype characterisation Quantification of number of Golgi spots versus cell volume Average number of Fringe-GFP spotsa Cell volume (10 μm3) Number of Fringe-GFP spotsa/10 μm3 −ds RNA 20.2±5.9 492±174 0.43±0.15 +ds CG10117 (MG+++) 37.3±14.6 1159±643 0.36±0.08 +ds CG32703 (MG++) 30.0±10.6 593±282 0.58±0.26 +ds wnd (LS) 17±8.8 490±210 0.40±0.19 Characterization of candidates Gene targeted tER-Golgi phenotype Cell proliferation Cell cycle Delta transport Average cell diameter Lipid droplets TOR activation No dsRNA Normal ✓ ✓ ✓ 100 ✓ No GFP Normal ✓ ✓ ✓ 99.9±0.2 ✓ No CG10177 MG+++ ✓ ✓ mostly OK 110.6±4.2** ✓ No CG10738 MG+++ ↓↓ ↑ G1# mostly OK 104.8±0.9** ✓ No mbt MG+++ ✓ ✓ ✓ 110.8±3.4** ✓ No cad96Ca MG++ ✓ ✓ ✓ 104.9±0.9** ↓✓ No CG32703 MG++ ✓ ✓ ✓ 107.1±1.7** ↓ No CG7097 MG++ ✓ ✓ ✓ 103.2±3.3 ✓ ire-1 MG++ ✓ ✓ ✓ 103.4±2.1 ↓✓ No PKC98E MG++ ↑ ✓ ✓ 103.4±1.6 ↑ No rhoGAP1A MG++ ✓ ✓ ✓ 105.7±4.6* ✓ CG4041 LS; Aggr ✓ ✓ ✓ 99.9±2.3 ✓ wallenda LS; Aggr ✓ ✓ ✓ 99.4±3.1 ✓ cdc25 MG++++ ↓↓ ↑↑ S/G2/M# ↑ in some cases 121.6±6.3** ✓ No myb MG+++/++++ ↓↓ ↑↑ S/G2/M# ND 112.1±3.6** Nd Metaphase tER-Golgi haze ND ND ND 132.1±1.7** Nd a The Fringe-GFP spots represent the Golgi (Kondylis et al, 2007). ± represents standard deviation. The hits highlighted in bold are further analysed (Supplementary Table S5). tER-Golgi phenotype: MG, More tER-Golgi units. The phenotype ranges from ++++ (strongest) to + (marginal). LS, Less tER-Golgi units (see also Supplementary Table S2). Cell proliferation: Arrows indicate statistically significant decrease or increase in cell proliferation compared with mock-treated cells (see also Figure 2B). Cell cycle: Conditions resulting in a statistically significant G1 or S/G2/M block are indicated with arrows. #Indicates conditions with increased sub-G1 cell population. Average cell diameter (normalized to mock-treated cells): Values marked by 1 or 2 asterisks indicate hits with P<0.05 and red asterisks indicate hits with 0.05<P<0.15. Lipid droplets: Arrows indicate statistically significant decrease or increase in lipid droplet number compared with mock-treated cells. Arrows with ✓ indicate a decrease in lipid droplet number but below statistical significance (0.05<P ADF (green) does not lead to Sec16 dispersion (red). 2D projections of confocal sections are presented in (A) and the first panel in (B). (C) Quantification of Sec16 dispersion upon expression of WT and K54R ERK7-V5. (D, E) Localization of Sec23 by Immunoelectron microscopy (IEM) in untransfected cells (D) and ERK7-V5 expressing cells (E). Note that the tER-Golgi units in (D) (between brackets) are largely absent in (E) (arrowheads), Sec23 (red circles) is dispersed and largely absent from the remnants of tER-Golgi units, and ERK7 is sometimes present in small aggregates (arrows). Scale bars: 5 μm (A, B); 200 nm (D, E). Download figure Download PowerPoint Serum and amino-acid starvation induce tER site disassembly In contrast to well-studied MAPK members, such as ERK1/2, p38s and JNKs that are activated by growth factors and MAPKKs, the regulation of ERK7 activity and its physiological functions is much less explored. In particular, rat ERK7 seems to be autoactivated (Abe et al, 1999, 2001; Klevernic et al, 2006). Furthermore, rat ERK7 has relatively high, constitutive kinase activity, which is not further stimulated by the addition of serum or EGF and not inhibited by classical MEK inhibitors, such as U0126, PD98059 and YOPJ (Abe et al, 2001). A similar behaviour to growth factor signalling has also been reported for the human homologue of rat ERK7 (hERK8) in HEK-293 cells (Klevernic et al, 2006; Erster et al, 2010), although this does not seem to be the case in COS cells (Abe et al, 2002). To investigate whether serum components influence the effect of ERK7 on tER sites in S2 cells, ERK7 was overexpressed in cells that were serum starved, resulting in a Sec16 dispersion as strong as in fed cells (Figure 6C). However, we noticed that serum starvation alone affected the tER site organization and Sec16 distribution. Strikingly, removing the serum from the culture medium for 5–7 h resulted in the large displacement of Sec16 from tER sites (Figure 4A), but not its degradation (Figure 4B), resulting in the disorganization of the tER-Golgi unit (Figure 4E, arrowhead), phenocopying ERK7 overexpression. In agreement with tER site disassembly and loss of Sec16 (Ivan et al, 2008), serum deprivation was accompanied by an inhibition in secretion as the delivery of the plasma membrane protein Delta was strongly impaired (Supplementary Figure S6A). The t
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