Quantitative modelling of amyloidogenic processing and its influence by SORLA in Alzheimer's disease
2011; Springer Nature; Volume: 31; Issue: 1 Linguagem: Inglês
10.1038/emboj.2011.352
ISSN1460-2075
AutoresVanessa Schmidt, Katharina Baum, Angelyn R. Lao, Katja Rateitschak, Yvonne Schmitz, Anke Teichmann, Burkhard Wiesner, Claus Munck Petersen, Anders Nykjær, Jana Wolf, Olaf Wolkenhauer, Thomas E. Willnow,
Tópico(s)Bioinformatics and Genomic Networks
ResumoArticle11 October 2011free access Quantitative modelling of amyloidogenic processing and its influence by SORLA in Alzheimer's disease Vanessa Schmidt Vanessa Schmidt Max-Delbrück-Center for Molecular Medicine, Berlin, Germany Search for more papers by this author Katharina Baum Katharina Baum Max-Delbrück-Center for Molecular Medicine, Berlin, Germany Search for more papers by this author Angelyn Lao Angelyn Lao Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, Rostock, Germany Search for more papers by this author Katja Rateitschak Katja Rateitschak Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, Rostock, Germany Search for more papers by this author Yvonne Schmitz Yvonne Schmitz Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, Rostock, Germany Search for more papers by this author Anke Teichmann Anke Teichmann Leibniz-Institut für Molekulare Pharmakologie, Berlin, Germany Search for more papers by this author Burkhard Wiesner Burkhard Wiesner Leibniz-Institut für Molekulare Pharmakologie, Berlin, Germany Search for more papers by this author Claus Munck Petersen Claus Munck Petersen MIND Center, Department of Biomedicine, University of Aarhus, Aarhus C, Denmark Search for more papers by this author Anders Nykjaer Anders Nykjaer MIND Center, Department of Biomedicine, University of Aarhus, Aarhus C, Denmark Search for more papers by this author Jana Wolf Jana Wolf Max-Delbrück-Center for Molecular Medicine, Berlin, Germany Search for more papers by this author Olaf Wolkenhauer Corresponding Author Olaf Wolkenhauer Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, Rostock, Germany Stellenbosch Institute for Advanced Study (STIAS), Stellenbosch, South Africa Search for more papers by this author Thomas E Willnow Corresponding Author Thomas E Willnow Max-Delbrück-Center for Molecular Medicine, Berlin, Germany Search for more papers by this author Vanessa Schmidt Vanessa Schmidt Max-Delbrück-Center for Molecular Medicine, Berlin, Germany Search for more papers by this author Katharina Baum Katharina Baum Max-Delbrück-Center for Molecular Medicine, Berlin, Germany Search for more papers by this author Angelyn Lao Angelyn Lao Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, Rostock, Germany Search for more papers by this author Katja Rateitschak Katja Rateitschak Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, Rostock, Germany Search for more papers by this author Yvonne Schmitz Yvonne Schmitz Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, Rostock, Germany Search for more papers by this author Anke Teichmann Anke Teichmann Leibniz-Institut für Molekulare Pharmakologie, Berlin, Germany Search for more papers by this author Burkhard Wiesner Burkhard Wiesner Leibniz-Institut für Molekulare Pharmakologie, Berlin, Germany Search for more papers by this author Claus Munck Petersen Claus Munck Petersen MIND Center, Department of Biomedicine, University of Aarhus, Aarhus C, Denmark Search for more papers by this author Anders Nykjaer Anders Nykjaer MIND Center, Department of Biomedicine, University of Aarhus, Aarhus C, Denmark Search for more papers by this author Jana Wolf Jana Wolf Max-Delbrück-Center for Molecular Medicine, Berlin, Germany Search for more papers by this author Olaf Wolkenhauer Corresponding Author Olaf Wolkenhauer Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, Rostock, Germany Stellenbosch Institute for Advanced Study (STIAS), Stellenbosch, South Africa Search for more papers by this author Thomas E Willnow Corresponding Author Thomas E Willnow Max-Delbrück-Center for Molecular Medicine, Berlin, Germany Search for more papers by this author Author Information Vanessa Schmidt1, Katharina Baum1,‡, Angelyn Lao2,‡, Katja Rateitschak2, Yvonne Schmitz2, Anke Teichmann3, Burkhard Wiesner3, Claus Munck Petersen4, Anders Nykjaer4, Jana Wolf1, Olaf Wolkenhauer 2,5 and Thomas E Willnow 1 1Max-Delbrück-Center for Molecular Medicine, Berlin, Germany 2Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, Rostock, Germany 3Leibniz-Institut für Molekulare Pharmakologie, Berlin, Germany 4MIND Center, Department of Biomedicine, University of Aarhus, Aarhus C, Denmark 5Stellenbosch Institute for Advanced Study (STIAS), Stellenbosch, South Africa ‡These authors contributed equally to this work *Correspondence to: [email protected]@mdc-berlin.de The EMBO Journal (2012)31:187-200https://doi.org/10.1038/emboj.2011.352 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 The extent of proteolytic processing of the amyloid precursor protein (APP) into neurotoxic amyloid-β (Aβ) peptides is central to the pathology of Alzheimer's disease (AD). Accordingly, modifiers that increase Aβ production rates are risk factors in the sporadic form of AD. In a novel systems biology approach, we combined quantitative biochemical studies with mathematical modelling to establish a kinetic model of amyloidogenic processing, and to evaluate the influence by SORLA/SORL1, an inhibitor of APP processing and important genetic risk factor. Contrary to previous hypotheses, our studies demonstrate that secretases represent allosteric enzymes that require cooperativity by APP oligomerization for efficient processing. Cooperativity enables swift adaptive changes in secretase activity with even small alterations in APP concentration. We also show that SORLA prevents APP oligomerization both in cultured cells and in the brain in vivo, eliminating the preferred form of the substrate and causing secretases to switch to a less efficient non-allosteric mode of action. These data represent the first mathematical description of the contribution of genetic risk factors to AD substantiating the relevance of subtle changes in SORLA levels for amyloidogenic processing as proposed for patients carrying SORL1 risk alleles. Introduction According to the amyloid hypothesis, the extent of proteolytic processing of the amyloid precursor protein (APP) into amyloid-β (Aβ) peptides is central to the pathology of Alzheimer's disease (AD) (Haass and Selkoe, 2007). Support for this concept not only comes from familial cases of AD in which mutations in the genes encoding APP and presenilins profoundly alter APP processing fates (Hardy, 2009). This hypothesis is also supported by data from sporadic forms of the disease where more subtle changes in the activity of modifiers are believed to promote production of neurotoxic Aβ species. A number of modifier genes have been shown to affect APP processing fates including LRP1 (Pietrzik et al, 2002), Nogo-66 receptor (Park and Strittmatter, 2007), or Bri2 (Matsuda et al, 2005), just to name a few. However, their relevance for AD has mostly been investigated in transgenic cell lines and in knockout mouse models. While these studies were important to identify basic concepts in APP processing, experimental systems with massive overexpression or complete lack of modifier activity fall short of modelling the in vivo situation in patients where modest alterations in activity may affect neuropathology over the course of a life time. Sorting protein-related receptor with A-type repeats (SORLA; also known as SORL1 or LR11) is a neuronal receptor for APP that controls intracellular transport and processing of the precursor protein (Andersen et al, 2005; Offe et al, 2006; Schmidt et al, 2007). In cultured cells, SORLA impairs the initial cleavage of APP by α- and β-secretases, blocking amyloidogenic and non-amyloidogenic processing pathways alike (Schmidt et al, 2007). Since expression of SORLA is reduced in the brain of some patients with sporadic AD (Dodson et al, 2006) and because SORL1 gene variants are associated with occurrence of the disease (Rogaeva et al, 2007), the receptor is considered a major risk gene in late-onset AD (Bertram et al, The AlzGene Database; http://www.alzgene.org). To test the relevance of even subtle alterations in SORLA levels for amyloidogenic processing, we developed a unique cell system, where we can vary the molar ratio of APP and SORLA over a continuous range of concentrations—a model that more truly reflects the situation in AD patients than knockout or overexpression experiments. In a systems biology approach, we combined quantitative biochemical studies with mathematical modelling to establish the first kinetic model of APP processing and its influence by SORLA. Our data demonstrate that α- and β-secretases are allosteric enzymes that depend on the formation of APP oligomers for efficient processing. Cooperativity in APP binding enables swift adaptive changes in enzyme activity with even small alterations in substrate concentration. We also demonstrate that SORLA does not affect the enzymatic activity of secretases per se. Rather, it acts as inhibitor that prevents APP oligomerization, thereby eliminating the preferred form of the secretase substrate and reducing amyloidogenic processing. Results Application of Tet-off system to modulate SORLA and APP expression levels We have applied the tetracycline-controlled transactivator (tTA) system to develop a cellular model with tightly controlled expression of APP and SORLA over a wide range of molar concentrations. In the Tet-off version of this system (Gossen and Bujard, 1992), constitutive expression of transgenes (such as APP and SORLA) is driven from a regulatory site composed of the modified tetracycline-response element (TREMOD) and a minimal CMV promoter (Figure 1A). Transcription requires binding of tTA to TREMOD. The application of doxycycline releases tTA from the promoter and shuts off gene transcription in a dose-dependent manner (Figure 1A). The Tet-off system worked faithfully to vary the molar concentrations of APP and SORLA in Chinese hamster ovary (CHO) cells transfected with the respective expression constructs. Thus, application of doxycycline resulted in stable reduction of APP and SORLA protein levels in cells after 24 h (Figure 1B). As shown by immunofluorescence microscopy (Supplementary Figure S1), the recombinant proteins co-localized to the perinuclear (Golgi) region, the main cellular compartment where both proteins interact in cultured cells and in tissues in vivo (Offe et al, 2006; Rogaeva et al, 2007; Schmidt et al, 2007). Doxycycline-induced repression of one of the proteins (here APP) did not affect expression and subcellular localization of the constitutively expressed partner (here SORLA; Supplementary Figure S1B). Figure 1.Tet-off system to modulate cellular expression of APP and SORLA. (A) Strategy for doxycycline-dependent repression of APP and SORLA expression using the Tet-off system. tTA, tetracycline-controlled transactivator. (B) CHO cells were stably transfected with Tet-off constructs for expression of SORLA and APP. Following treatment with 1 ng/ml doxycycline for the indicated periods of time, expression levels of both proteins were determined by western blot analysis as exemplified in the inset. Intensities of the immunoreactive bands corresponding to SORLA and APP were quantified by densitometric scanning in replicate blots (n=3), and expressed as relative levels compared with untreated cells (set at 100%). Error bars are smaller than the actual data symbols shown. Download figure Download PowerPoint Next, expression constructs for doxycycline-regulatable expression of APP and SORLA were stably introduced into parental CHO cells or into CHO cells constitutively expressing human SORLA (CHO-S) or APP695 (CHO-A) (Schmidt et al, 2007). All in all, three types of cell lines were generated: (i) CHO pTet-APP to control APP expression in the absence of SORLA, (ii) CHO-S pTet-APP to regulate APP levels at constant concentrations of SORLA, and (iii) CHO-A pTet-SORLA to regulate SORLA levels at a fixed concentration of APP (Figure 2A). Applying a range of doxycycline concentrations from 0.025 to 10 ng/ml for 48 h enabled us to modify the cellular concentrations of APP (from pTet-APP) and of SORLA (from pTet-SORLA) in a dose-dependent manner as shown by western blot analysis (Figure 2A) and ELISA (Figure 2B). Down-regulation worked best for pTet-APP with a 90% reduction of expression at 10 ng/ml of doxycycline. pTet-SORLA levels were reduced by ∼50% at maximal doxycycline concentration (Figure 2B). The residual levels of APP and SORLA seen in transfectants at 10 ng/ml of doxycycline (Figure 2A) represented leaky expression from the Tet-off constructs as no signals corresponding to the endogenous proteins were seen in parental CHO cells (Supplementary Figure S2). Accordingly, all analyses in this study relate to the cellular interaction of human APP and human SORLA. Figure 2.CHO cell models with regulatable expression of SORLA and APP. (A) Parental CHO cells or CHO cells constitutively expressing human SORLA (CHO-S) or APP695 (CHO-A) were stably transfected with Tet-off constructs for APP (pTet-APP) or SORLA (pTet-SORLA). Protein expression was detected in lysates from cells treated with the indicated concentrations of doxycycline for 48 h. (B) Quantification by ELISA of APP and SORLA expressed from the Tet-off constructs following doxycycline treatment (four independent repeats). Protein levels are indicated as percent of levels seen in the untreated conditions (set at 100%). Download figure Download PowerPoint SORLA alters the mode of secretase action from allosteric to non-allosteric Initially, we applied cell lines CHO pTet-APP and CHO-S pTet-APP to describe the kinetics of α- and β-secretase activities in our cell model in the presence or absence of SORLA. Replicate cell layers were subjected to a range of doxycycline concentrations and the concentration of the substrate APP in the corresponding cell lysates was correlated with the rate of production of soluble (s) APPα and sAPPβ, the products of α- and β-secretase activities, respectively. Surprisingly, in cells without SORLA (CHO pTet-APP), the Michaelis–Menten equation failed to correctly describe the enzyme kinetics as shown by the deviation of data points from the calculated hyperbolic curve (closed symbols, Figure 3A and B), and by the high absolute error (Table IA). Instead, a more accurate fit of the data points (closed symbols, Figure 3C and D) and a reduction in absolute error (Table IA) was obtained when we used a Hill equation. Hill kinetics describe cooperativity in (allosteric) enzyme activity and are characterized by a sigmoidal relationship of substrate concentration with enzyme reaction rate. The Hill coefficient, describing the degree of cooperativity, was 2 for both α- and β-secretases, suggesting dimerization of enzymes or substrate (or both) as determinant of enzyme kinetics (Table IA). Figure 3.Enzyme kinetics of soluble APP production in the presence or absence of SORLA. CHO pTet-APP (w/o SORLA) and CHO-S pTet-APP (w/ SORLA) cells were treated with a concentration range of 0.025–10 ng/ml of doxycycline for 48 h. Subsequently, concentrations of APP in cell lysates and total amount of soluble (s) APPα (A, C) and sAPPβ (B, D) secreted into the medium within 24 h were determined by ELISA. Enzyme kinetics of APP turnover into sAPP products was calculated using Michaelis–Menten (A, B) or Hill equations (C, D). Log scale presentation was chosen to better illustrate the deviation of data points from the calculated curve for CHO pTet-APP (closed symbols) for Michaelis–Menten kinetics (A, B) compared with the Hill equation (C, D). Download figure Download PowerPoint Table 1. Comparison of Hill and Michaelis–Menten kinetics for α- and β-secretases in cells without (A) and with (B) SORLA Michaelis–Menten Hill α-Secretase β-Secretase α-Secretase β-Secretase (A) w/o SORLA Absolute error 13 940.0 45.9 3306.8 19.0 Hill coefficient — — 2.21 2.09 (B) w/ SORLA Absolute error 573.7 28.8 573.1 28.4 Hill coefficient — — 0.94 0.79 The presence of SORLA (in CHO-S pTet-APP) reduced the total amount of soluble APP products generated from APP drastically as compared with CHO pTet-APP (Figure 4). At half-maximal velocity (V0.5) of α-secretase, the amount of sAPPα produced from 106.9 nM APP was reduced from 97.2 to 2.7 fmol/h in the presence of 120 nM SORLA (97% reduction) (Figure 4A). Similarly, the amount of sAPPβ produced from 62.6 nM APP (V0.5 of β-secretase) was reduced from 4.6 to 1.1 fmol/h in the presence of 120 nM SORLA (75% reduction) (Figure 4B). Figure 4.Inhibitory effect of SORLA on soluble APP production. CHO pTet-APP (w/o SORLA) and CHO-S pTet-APP (w/ SORLA) cells were treated with a concentration range of 0.025–10 ng/ml doxycycline for 48 h. Then, concentrations of APP in the cell lysates and total amount of sAPPα (A) and sAPPβ (B) secreted into the medium within 24 h were determined by ELISA. Enzyme kinetics were calculated using a Hill equation. These data are identical to the data points shown in Figure 3 but linear presentation was chosen here to better illustrate the dramatic decrease in APP processing in cells expressing SORLA (open symbols) compared with parental CHO cells (closed symbols). Download figure Download PowerPoint Surprisingly, the presence of SORLA also profoundly changed the mode of secretase action from allosteric to non-allosteric. Thus, the requirement for cooperativity was lost when cells expressed the receptor as shown by a similar curve fit and identical absolute errors when applying Michaelis–Menten (open symbols, Figure 3A and B; Table IB) or Hill (open symbols, Figure 3C and D; Table IB) equations. Lack of cooperativity in APP processing with SORLA was also confirmed by Hill coefficients of 0.97 and 0.79 for α- and β-secretases, respectively (Table IB). The necessity for cooperativity and loss thereof in the presence of SORLA was also seen when studying the kinetics of Aβ production in CHO pTet-APP versus CHO-S pTet-APP cells. As has been observed for α- and β-secretases above (Figure 3), Hill but not Michaelis–Menten equations correctly described the γ-secretase kinetics in the absence of the SORLA (closed symbols, Figure 5A versus B). In contrast in the presence of SORLA, a similar curve fit was obtained when applying Michaelis–Menten (open symbols, Figure 5A) or Hill (open symbols, Figure 5B) equations. Overall, the extent of Aβ40 production at 35.4 nM APP (V0.5) was reduced from 18.7 fmol/h in CHO pTet-APP to 0.77 fmol/h in the presence of 120 nM SORLA in CHO-S pTet-APP (96% reduction) (Figure 5C). Figure 5.Kinetics of Aβ40 peptide production in the presence or absence of SORLA. (A, B) CHO pTet-APP (w/o SORLA) and CHO-S pTet-APP (w/ SORLA) cells were treated with a concentration range of 0.025–10 ng/ml doxycycline for 48 h. Subsequently, concentrations of APP in the cell lysates and the total amount of Aβ40 secreted into the medium within 24 h were determined by ELISA. Enzyme kinetics of substrate APP turnover into Aβ40 was calculated using Michaelis–Menten (A) or Hill equations (B). Log scale presentation was chosen to better illustrate the deviation of data points from the calculated curve for CHO pTet-APP (closed symbols) for Michaelis–Menten kinetics (A) compared with the Hill equation (B). (C) Enzyme kinetics as in (B) were calculated using a Hill equation. However, linear presentation of data points was chosen to better illustrate the dramatic decrease in Aβ40 production in cells expressing SORLA (open symbols) compared with parental CHO cells (closed symbols). Download figure Download PowerPoint Inverse correlation between SORLA activity and APP processing rates Previous studies had demonstrated a 25% reduction in SORLA levels in the brain of some patients suffering from sporadic AD (Scherzer et al, 2004). Whether such modest alterations in receptor level may have any functional consequences for amyloidogenic processing remained controversial. To unambiguously test the relevance of even small variations in SORLA activity for APP processing, we applied another cell line CHO-A pTet-SORLA. In this cell model, the ratio of substrate APP and secretases is kept constant but the molar concentration of the inhibitor SORLA is altered. Although the molar ratio of doxycycline-regulatable SORLA to constitutively expressed APP could only be varied 2.5-fold (50% reduction in SORLA), a clear inverse correlation of SORLA levels with rates of APP processing into sAPPα, sAPPβ, and Aβ40 was seen along the entire concentration range (Figure 6). Figure 6.SORLA is a dose-dependent inhibitor of APP processing in CHO cells. CHO-A pTet-SORLA cells were treated with a concentration range of 0.025–60 ng/ml doxycycline for 48 h. Thereafter, the concentrations of SORLA in cell lysate and of the total amounts of soluble (s) APPα, sAPPβ, and of Aβ40 secreted into the medium within 24 h were determined by ELISA. Linear regression analysis demonstrates a statistically significant linear decrease in the production of sAPPα (A), sAPPβ (B), and Aβ40 (C), with increasing SORLA concentrations in the cells. Download figure Download PowerPoint To test whether a similar inverse relationship between SORLA activity and APP processing is also seen for the endogenous proteins in neurons, we established siRNA knockdown of receptor expression in the human neuroblastoma cell line SH-SY5Y that expresses endogenous levels of SORLA and APP (Figure 7A, inset). As shown for CHO cells above (Figure 6), a clear inverse correlation of SORLA levels with production rates for sAPPα, sAPPβ, and Aβ40 was also observed in SH-SY5Y (Figure 7). These data substantiated the relevance of subtle changes in SORLA activity for the extent of APP processing as proposed for patients carrying SORL1 risk alleles (Scherzer et al, 2004; Rogaeva et al, 2007). Figure 7.Endogenous SORLA is a dose-dependent inhibitor of APP processing in neuronal cells. (A–C) Knockdown by siRNA approach was used to modulate the levels of SORLA in the human neuroblastoma cell line SH-SY5Y that expresses SORLA and APP endogenously. Concentration of SORLA in replicate cell lysates and of the respective levels of soluble (s) APPα, sAPPβ, and of Aβ40 secreted into the medium within 24 h were determined by ELISA. Linear regression analysis demonstrates a statistically significant linear decrease in the production of sAPPα (A), sAPPβ (B), and Aβ40 (C), with increasing SORLA concentrations in the cells. (Inset in A) The amount of endogenous APP and SORLA in lysates of cells treated with (w/) or without (w/o) siRNA is shown by western blot analysis. Multiple immunoreactive bands for APP correspond to the immature and mature precursor variants. Detection of actin was used as a loading control. Download figure Download PowerPoint SORLA does not directly impact enzymatic activity of secretases So far, our data provided evidence for cooperativity in cleavage of APP by α-, β-, and γ-secretases, and the ability of SORLA to interfere with processing by preventing cooperativity in a dose-dependent manner. Conceptually, these data may be explained by direct binding of SORLA to APP or to the secretases preventing oligomerization of substrate and/or enzymes. The ability of SORLA to bind to APP had been established in vitro and in cells by us and others before (Andersen et al, 2005, 2006; Offe et al, 2006; Spoelgen et al, 2006). Here, we explored whether the receptor may also directly interact with secretases to block their enzymatic activity. To do so, we subjected cell lysates of parental CHO cells or CHO-S cells (containing 120 nM SORLA) to cell-free secretase assays. The total amount of tumour necrosis factor converting enzyme (TACE), of ADAM10, and of β-site of APP cleaving enzyme (BACE1) expressed in both cell lines was identical (Figure 8, insets). Also, both cell lines showed similar activities for α-secretase (Figure 8A) and β-secretase (Figure 8B) when applying artificial protease substrates (as described in the Supplementary data). Figure 8.Influence of SORLA on α- and β-secretase activities. Extracts from parental CHO cells and from cells constitutively expressing 120 nM SORLA (CHO-S) were subjected to cell-free α- (A) and β-secretase (B) activity measurement using commercially available assays (four independent repeats). (Insets) The amount of proposed α-secretases TACE and ADAM10, and of β-secretase BACE1 in both cell lines was evaluated in replicate samples by western blotting. Detection of actin was used as a loading control. Download figure Download PowerPoint We also tested the activity of γ-secretase by transiently transfecting expression constructs encoding the carboxyl terminal fragments (CTFs) C83 and C99 into CHO and CHO-S cell lines (Figure 9). C83 and C99 are γ-secretase substrates derived from cleavage of the APP holoprotein by α- and β-secretase, respectively. Forty-eight hours after transfection, cell extracts from CHO and CHO-S cells were generated and incubated for 2 h at 37 °C to determine turnover of CTFs into the APP intracellular domain (AICD) (Figure 9A). Densitometric scanning of replicate western blot experiments demonstrated identical γ-secretase activities in cells with or without SORLA (Figure 9B). Figure 9.Influence of SORLA on γ-secretase activity. (A) Parental CHO cells (lanes 1 and 2) and CHO cells expressing 120 nM SORLA (CHO-S; lanes 3 and 4) were transiently transfected with expression constructs for C99 or C83 for 48 h. Thereafter, cell lysates were either kept on ice (0 h time point) or incubated for 2 h at 37 °C, and the amount of substrates C99/C83 and of the product AICD in the extracts were determined by western blot. (B) The intensity of immunoreactive bands representing the indicated proteins were quantified by densitometric scanning and expressed as relative ratio at 2 versus 0 h of incubation. The stippled line indicates a ratio of 1 (2 h/0 h), assuming the absence of γ-secretase activity (three independent repeats). Download figure Download PowerPoint SORLA disrupts oligomerization of APP Taken together, our data demonstrated that SORLA acts as inhibitor of amyloidogenic processing mainly by functionally interacting with APP. Given the requirement for cooperativity in APP processing, we speculated that efficient proteolytic breakdown of APP requires oligomerization of the precursor protein and that binding of SORLA to APP may prevent this from happening. Our hypothesis was confirmed when we studied APP oligomerization in CHO cells in the presence or absence of SORLA. Thus, we transiently expressed a fusion protein of APP with enhanced green fluorescence protein (EGFP) in CHO and CHO-S cells and subjected cell extracts of the transfectants to native polyacrylamide gel electrophoresis (PAGE). Detection of APP–EGFP by fluorescence scanning of the gels or by immunodetection using anti-GFP antiserum demonstrated the presence of monomeric and oligomeric variants of APP–EGFP in parental CHO cells. However, no APP–EGFP oligomers were seen in CHO-S (Figure 10A). The ability of SORLA to block APP homomer formation was substantiated in neuronal cells using fluorescence correlation spectroscopy (FCS). FCS is an analytic tool that enables both qualitatively and quantitatively, examining the molecular dynamics of protein–protein interactions by determining the fluctuation in fluorescence intensities of moving fluorescent molecules (Elson, 2001). When APP–GFP and APP–RFP were co-expressed in parental SH-SY5Y cells, the normalized cross-correlation curve indicated a substantial extent of heterodimer formation between the two APP variants (Figure 10B). In contrast, in SH-SY5Y cells expressing a SORLA transgene cross-correlation represented random noise, demonstrating independent fluctuation of the two protein species (Figure 10C). Quantification of the extent of cross-correlation showed that 30% of all APP molecules represented APP–RFP/APP–GFP dimers in cells without SORLA. Cells expressing the receptor exhibited significantly reduced dimer formation (P=0.0067; inset in Figure 10C). Figure 10.SORLA prevents oligomerization of APP in cultured cells and the brain in vivo. (A) Parental CHO cells (lanes 1 and 2) and CHO cells expressing 120 nM SORLA (CHO-S; lanes 3 and 4) were transiently transfected with expression constructs for APP–EGFP for 48 h. Subsequently, cell lysates were subjected to native PAGE and fluorescence scanning to detect EGFP activity (upper panel) or western blot analysis using anti-EGFP antisera (middle panel). As a control, replicate cell lysates were subjected to denaturing SDS–PAGE and western blotting with anti-GFP antisera (lower panel). Filled arrowheads indicate monomeric, open arrowheads present multimeric APP–EGFP forms. (B, C) SH-SY5Y cells were co-transfected with expression constructs for APP–GFP and APP–RFP. Two days later, cells were subjected to live cell imaging using FCS. Autocorrelation curves for APP–RFP (red lines) and APP–GFP (green lines) as well as for
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