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. 2012 Jan 4;31(1):187-200.
doi: 10.1038/emboj.2011.352. Epub 2011 Oct 11.

Quantitative modelling of amyloidogenic processing and its influence by SORLA in Alzheimer's disease

Vanessa Schmidt  1 , Katharina Baum, Angelyn Lao, Katja Rateitschak, Yvonne Schmitz, Anke Teichmann, Burkhard Wiesner, Claus Munck Petersen, Anders Nykjaer, Jana Wolf, Olaf Wolkenhauer, Thomas E Willnow
Affiliations

Quantitative modelling of amyloidogenic processing and its influence by SORLA in Alzheimer's disease

Vanessa Schmidt et al. EMBO J. .

Abstract

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.

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Figure 1
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.
Figure 2
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%).
Figure 3
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).
Figure 4
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).
Figure 5
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).
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.
Figure 7
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.
Figure 8
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.
Figure 9
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).
Figure 10
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 cross-correlation of fluorescence intensities in APP–GFP/APP–RFP heterodimers (black lines) are shown. The normalized cross-correlation curve in the absence of SORLA indicates substantial APP–GFP/APP–RFP heterodimer formation (B). In contrast, in SH-SY5Y cells expressing a SORLA transgene (SY5Y-S) (C), cross-correlation represents random noise, suggesting independent fluctuation of the two protein species. Using the ZEN Software 2010, the percent of dimer formation was shown to be reduced from 30 to 15% in the presence of SORLA (inset in C) (three independent repeats). (D) APP was immunoprecipitated from replicate brain samples from wild-type (lanes 1 and 2) or SORLA-deficient mice (lanes 3 and 4) and resolved by Blue Native PAGE. Immunoreactive bands corresponding to a low-molecular weight (filled arrowhead) and a high-molecular weight complex of APP (open arrowhead) are detected in wild-type tissues. Only the high-molecular weight complex of APP is seen in brain lacking SORLA (open arrowhead in lanes 3 and 4).
Figure 11
Figure 11
Mathematical modelling of APP processing and its influence by SORLA. (A) Biochemical network of the interaction of reactants APP (blue symbol) with α- and β-secretases (green symbols) and the formation of amyloidogenic and non-amyloidogenic products (orange symbols). Complexes of APP and secretases are indicated as white boxes and complexes of APP and SORLA as grey box. Processing of monomeric (upper panel) and of dimeric forms of APP (lower panel) is indicated as separate pathways. The two modules are linked together by the reversible dimerization-dissociation of APP and secretases. Interaction of SORLA with monomeric APP (grey box) has two consequences. It prevents formation of APP dimers, the preferred secretase substrates, and it lessens the amount of APP monomers available for processing. Note that dissociation of homodimers of APP (APPd), α-secretase (αd), and β-secretase (βd) results in two identical monomers of the respective proteins. See Supplementary data for a detailed description of the variables used in the biochemical network. The diagram was produced with Cell Designer 4.0 (Cell Designer, Tokyo, Japan: The Systems Biology Institute; 2008) (Kitano et al, 2005). (B–E) Simulation results of the mathematical model (solid lines) for the various APP processing products are shown together with the actual data points obtained in biochemical experiments. The total amount of products (black line) is the sum of the products produced in the monomer (red line) and dimer processing (green line) pathways. In the absence of SORLA, the ‘dimer processing’ more closely resembles the combined model for sAPPα (B) and sAPPβ (C). In contrast, in the presence of SORLA, it is the ‘monomer processing’ that closely resembles the combined model for both processing products (D, E). In (E), black and red lines are superimposed.
Figure 12
Figure 12
APP processing at intermediate levels of SORLA. (A, B) Simulations of the influence of intermediate levels of SORLA on APP processing into sAPPα (A) and sAPPβ (B) are shown. The stippled black lines in (A, B) represent the values for maximum levels of SORLATot (5.13 × 105 fmol) as in CHO-S pTet-APP (set at arbitrary value 1 × SORLATot). The solid black lines represent the situation in the absence of SORLA as in CHO pTet-APP (set at 0 × SORLATot). Simulations of total processing for three intermediates levels of SORLA (3, 12, and 30% of SORLATot) were calculated as detailed in the Supplementary data, and are shown as blue stippled lines. (C–H) Simulation curves for APP processing into sAPPα and sAPPβ for intermediate levels of 3% (C, D), 12% (E, F), and 30% of SORLATot (G, H) are given. Total processing (black lines) as well as dimer (green lines) and monomer (red lines) processing are indicated for each simulation. A switch from preferred dimer-to-monomer processing is seen at 0.12 × SORLATot for both α-secretase (E) and β-secretase (F).
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References

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