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. Author manuscript; available in PMC: 2018 Feb 1.
Published in final edited form as: Discov Med. 2017 Feb;23(125):121–128.

Mechanisms that Synergistically Regulate η-Secretase Processing of APP and Aη-α Protein Levels: Relevance to Pathogenesis and Treatment of Alzheimer’s Disease

Joseph Ward 1, Haizhi Wang 2,3, Aleister J Saunders 2,3,4, Rudolph E Tanzi 1, Can Zhang 1
PMCID: PMC5524192  NIHMSID: NIHMS874673  PMID: 28371615

Abstract

The pathophysiology of Alzheimer’s disease (AD) is characterized by the formation of cerebral β-amyloid plaque from a small peptide amyloid- (Aβ). Aβ is generated from the canonical amyloid-β precursor protein (APP) proteolysis pathway through β- and γ-secretases. Decreasing Aβ levels through targeting APP processing is a very promising direction in clinical trials for AD. A novel APP processing pathway was recently identified, in which η-secretase processing of APP occurs and results in the generation of the carboxy-terminal fragment-η, (CTF-η or η-CTF) (Wang et al., 2015) and Aη-α peptide (Willem et al., 2015). η-Secretase processing of APP may be up-regulated by at least two mechanisms: either through inhibition of lysosomalcathepsin degradation pathway (Wang et al., 2015) or through inhibition of BACE1 that competes with η-secretase cleavage of APP (Willem et al., 2015). A thorough characterization of η-processing of APP is critical for a better understanding of AD pathogenesis and insights into results of clinical trials of AD. Here we further investigated η-secretase processing of APP using well-characterized cell models of AD. We found that these two mechanisms act synergistically toward increasing η-secretase processing of APP and Aη-α levels. Furthermore, we evaluated the effects of several other known secretase modulators on η-processing of APP. The results of our study should advance the understanding of pathophysiology of AD, as well as enhance the knowledge in developing effective AD treatments or interventions related to η-secretase processing of APP.

Introduction

Alzheimer’s disease (AD) is an insidious neurodegenerative disorder and the primary cause of dementia in the elderly. There is currently no cure available to modify disease progression. AD is clinically characterized by deterioration of memory and cognitive functions, progressive impairment of daily living activities, and neuropsychiatric symptoms (Cummings, 2004). The underlying mechanisms of AD at the cell and molecular levels are still not completely elucidated. However, considerable genetic, biochemical, molecular-biological, and pathological evidence supports the β-amyloid-cascade hypothesis, positing that the excessive accumulation of β-amyloid (Aβ) is the primary pathological event leading to AD (Gandy, 2005; Hardy and Selkoe, 2002; Tanzi and Bertram, 2005; Zhang et al., 2017).

Aβ is generated through the well-characterized canonical APP proteolysis pathway via a serial cleavage by β-and γ-secretases, and this process is prevented if the first cleavage of APP occurs through α-secretase (Figure 1) (Bertram and Tanzi, 2008; Kimberly et al., 2003; Sisodia and St George-Hyslop, 2002; Vassar et al., 1999). Particularly, α-secretase cleavage of APP produces sAPPα and the α-carboxy-terminal fragment (CTFα or C83). β-Secretase cleavage primarily produces sAPPβ and the β-carboxy-terminal fragment (CTFβ or C99). β-Secretase cleavage of APP may also occur between Tyr10 and Glu11 of Aβ which produces C89 and is also known as β’-secretase activity (Liu et al., 2002; Vassar et al., 1999). C83 and C99 can be further cleaved by γ-secretase to produce P3 or Aβ, respectively (Figure 1). All α-, β-, and γ-secretases are membrane-bound proteins. The functional proteins of α-, and β-secretases are ADAM10 (a disintegrin and metalloproteinase 10) and BACE1 (β-site APP cleaving enzyme 1), respectively. γ-Secretase is a heterogeneous protein complex, which contains at least four transmembrane proteins: presenilin, presenilin enhancer 2, nicastrin, and anterior pharynx-defective 1 (Bertram and Tanzi, 2008; Edbauer et al., 2003; Sisodia and St George-Hyslop, 2002).

Figure 1.

Figure 1

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APP processing. The familial AD gene APP encodes the amyloid-β precursor protein, which undergoes serial proteolytic cleavages and generates two small pathogenic proteins: Aβ and Aη-α. Specifically the cleavage of APP produces Aβ through β- and γ-secretases; or Aη-α through η- and α-secretases. Aβ peptides display several isoforms, e.g., Aβ38, Aβ40, and Aβ42. Aβ42 is prone to aggregation and forms oligomers and then β-amyloid plaque, while Aη-α does not directly appear in the β-amyloid plaque. Abbreviations: APP, amyloid-β precursor protein; CTF, carboxy-terminal fragment; AICD, APP-intracellular domain protein.

Very recently two independent studies identified a novel “eta-,” or η-secretase processing pathway of APP (Figure 1). Intriguingly, the pathway seems to occur through different mechanisms (Wang et al., 2015; Willem et al., 2015). First, our group reported that the 25 kDa, carboxy-terminal fragment (CTF) of η-secretase processing of APP, or APP-CTFη or CTFη, accumulates significantly through inhibition of lysosomal-cathepsins (Wang et al., 2015). Our study was then subsequently validated in an independent study, in which η-secretase is suggested to be rendered functional by MT5-MMP, a membrane-bound matrix metalloproteinase. Notably, CTFη can be further cleaved by α- or β-secretase, to generate Aη-α and Aη-β, respectively. Intriguingly, genetic and pharmacological inhibition of BACE1 results in robust increase of CTF-η and Aη-α. Furthermore, Aη-α can lower the long-term potentiation event through the hippocampal slices ex vivo, recapitulating the pathology of AD. However, it is still unknown whether the “eta-secretase” products of APP generated via these two mechanisms were identical. A further investigation of η-secretase is required to elucidate the pathogenesis of AD.

This study is aimed at further investigating APP η-secretase processing and generation of the pathogenic Aη-α peptide. We first validated these two reports on η-secretase processing of APP by examining the effects of two mechanisms side-by-side. Moreover, we asked whether other known pharmacological modulators of APP processing may affect η-secretase processing of APP. The results of our study should advance the understanding of the pathophysiology of AD, as well as provide insights into developing effective strategies for AD treatment or intervention.

Experimental Procedures

Cell culture

We utilized the well-characterized Chinese hamster ovary (CHO) cells containing the Indiana mutation in APP751 isoform (or 7PA2 cells) that were previously reported (Walsh et al., 2000; Welzel et al., 2014). These cells were cultured on regular tissue culture plates in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum, 2 mM L-glutamine, 100 units/ml penicillin, 100 µg/ml streptomycin, and 200 µg/ml G418.

Chemicals

The previously reported ALLN (also known as calpain inhibitor I or N-acetyl-Leu-Leu-Norleu-al) was from Sigma-Aldrich (St. Louis, MO, USA) (Wang et al., 2015). BACE1 inhibitor IV (labeled as BACE1i in context) was from EMD Millipore (Billerica, MA, USA). Phorbol 12-myristate 13-acetate (PMA) was from Sigma-Aldrich. It is a phorbol ester that stimulates α-secretase activity (Zhang et al., 2007). TAPI-1 (tumor necrosis factor-α protease inhibitor-1) was from Sigma-Aldrich that inhibits α-secretase mediated shedding of the APP ectodomain (Zhang et al., 2007). DAPT, also known as N-[N-(3,5-difluorophenacetyl)-Lalanyl]-S-phenylglycine tert-butyl ester, is a γ-secretase inhibitor (GSI) that inhibits γ-secretase processing of its substrates, including APP and Notch. DAPT was from EMD Millipore. GSM36 was utilized and reported previously (Choi et al., 2014; Wagner et al., 2014).

Antibodies

Both 6E10 and M3.2 antibodies were raised to target the first 16 amino acids of Aβ from the N-terminus and were previously used to detect CTFη, CTFβ, Aβ, and Aη-α (Veeraraghavalu et al., 2014; Zhang et al., 2010a; 2010b). The 6E10 antibody was purchased from Covance (1:1,000; Dedham, MA, USA) and the M3.2 antibody was purchased from BioLegend (1:1,000; San Diego, CA, USA). The G12A antibody for APP (rabbit polyclonal, clone C7 targeting amino acid residues 732–751 of APP751) was previously described (1:1,000) (Griciuc et al., 2013; Wang et al., 2015). It was used to detect full length APP and APP-CTFs via α-, β-, and γ-secretases. The sAPPβ antibody was from IBL (1:1,000), whose recognized epitope was the last 6 amino acids of sAPPβ (ISEVKM) (Zhang et al., 2010a; 2010d). The β-actin antibody (1:10,000; Sigma-Aldrich) and GAPDH antibody (1:1,000; Cell Signaling; Danvers, MA, USA) were used to detect reference house-keeping proteins in the Western blotting analysis. The HRP-conjugated secondary antibodies (anti-mouse and anti-rabbit; 1:10,000) were purchased from Pierce Biotechnology of Thermo Fisher Scientific (Waltham, MA, USA).

Cell lysis and protein extraction

Cells were harvested after treatment, utilizing the method previously described (Zhang et al., 2010a; 2010b; 2010c; 2010d; 2007). Briefly cells were lysed in M-PER (Mammalian Protein Extraction Reagent) (Thermo Fisher Scientific) with 1x Halt protease inhibitor cocktail (Thermo Fisher Scientific). The lysates were collected and centrifuged at 10,000 g for 20 minutes, pellets were discarded, and supernatants were transferred to a new Eppendorf tube for protein analysis. Protein concentrations were quantified by the BCA protein assay kit (Pierce) that was previously reported (Zhang et al., 2010a; 2010b; 2010c; 2010d; 2007).

Western blotting analysis

Western blotting analysis was carried out by the method previously described (Hiltunen et al., 2006; Zhang et al., 2007). Briefly, after protein extraction, an equal amount of protein was applied to electrophoresis using the Novex NuPAGE SDS-PAGE Gel System (Thermo Fisher Scientific), followed by membrane transfer, antibody incubation, and signal development. The housekeeping protein, β-actin or GAPDH, was used as an internal control. We used the LI-COR Odyssey Fc imaging system to develop the membranes and the software Image Studio (LI-COR, Lincoln, NE, USA) and ImageJ (NIH, Bethesda, MD, USA) to demonstrate and analyze the proteins of interest.

Data analysis

The pathogenic Aη-α protein levels were quantified using the ImageJ software. The levels of the samples from treatment groups, e.g., ALLN or BACE1 inhibitor, were further normalized to those from the control samples. The sample numbers for each group were at least 3 for each dose in single-dose experiments. The quantified results were demonstrated as means±SEM. We used the one-way analysis of variance, as appropriate, to determine whether there are any significant differences comparing treatment groups to control. P value≤0.05 was considered statistically significant.

Results

We used the well-characterized Chinese hamster ovary (CHO) cells containing the Indiana mutation in APP751 isoform (or 7PA2 cells) (Walsh et al., 2000; Welzel et al., 2014). The cells were treated with various doses of ALLN (0, 0.6 µM, 1.25 µM, 2.5 µM, 5 µM, and 10 µM) or 1.0 µM BACE1 inhibitor (labeled as BACE1i) and harvested after 24 hrs. Cell lysates or media were applied to the Western blotting analysis (WB) and probed with M3.2, 6E10, or sAPPβ. M3.2 or 6E10 was raised to target the first 16 amino acids of Aβ from the N-terminus and was previously reported (Veeraraghavalu et al., 2014; Zhang et al., 2010a; 2010b). Both antibodies from different companies were utilized to warrant the specificity of antibodies and proteins of interest. Both M3.2 and 6E10 were able to detect full length APP and various APP cleavage products, including CTFη, CTFβ, Aβ, and Aη-α (Veeraraghavalu et al., 2014; Zhang et al., 2010a; 2010b), but not CTFα, and were suitable to test the roles of ALLN and BACE1i on the generation of APP products via η-secretase processing.

We first showed that ALLN or BACE1i robustly changed η-processing of APP. These treatments increased Aη-α via the M3.2 antibody in cell lysates, but we did not observe Aη-α in cell medium in these experimental conditions. Specifically, we detected Aβ and CTFβ, as well as Aη-α and CTFη (Figure 2A) in cell lysates. The doublets of CTFβ may contain phosphorylated CTFβ or C99 and C89 (generated by β’-secretase) (Liu et al., 2002; Vassar et al., 1999). We showed that increased CTFη levels were observed with BACE1 inhibition in 7PA2 cells, in agreement with prior findings (Wang et al., 2015; Willem et al., 2015). We did not observe the same robust change in CTFη levels with increasing ALLN doses in these cells as in other cells (Wang et al., 2015), suggesting a cell-type dependent response. Furthermore, we showed that Aη-α levels were barely detectable in cells of control treatment, but were significantly increased in cells treated with ALLN or BACE1i. Importantly both CTFη and Aη-α proteins displayed the same relative molecular weights for the samples generated from ALLN and BACE1i treatments. Additionally, we found that 1 µM BACE1i significantly increased Aη-α levels by 20.1% (p<0.05) compared to control, and 10 µM ALLN significantly increased Aη-α levels by 59.1% (p<0.001; ANOVA) (Figure 2B). Furthermore, we utilized the sAPPβ antibody on the conditioned medium samples. We showed that ALLN treatment displayed a trend of slight increase in sAPPβ levels compared to control. We also demonstrated that BACE1i robustly decreased sAPPβ levels compared to control, as expected (Figure 2C).

Figure 2.

Figure 2

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Both ALLN and BACE1i increased the pathogenic Aη-α protein levels. Stably-transfected 7PA2 cells were treated with different doses of ALLN or 1 µM BACE1 inhibitor for 24 hrs. Cells were harvested and then cell lysates and conditioned medium were analyzed by WB. A. Cell lysates were subjected to WB and the M3.2 antibody was used for detection of APP-CTFβ/η, Aβ, and Aη-α. B. The pathogenic Aη-α protein levels were quantified and demonstrated via densitometry for samples in A. C. Conditioned medium was subjected to WB for the detection of sAPPβ with the sAPPβ antibody.

Besides ALLN and BACE1i, we investigated whether compounds that target α- and γ-secretase, which are potential therapeutic targets for AD, may affect η-secretase processing of APP and the pathogenic Aη-α levels. PMA, or phorbol 12-myristate 13-acetate, is a phorbol ester that stimulates α-secretase activity (Zhang et al., 2007). TAPI-1 (tumor necrosis factor-α protease inhibitor-1) inhibits α-secretase mediated shedding of the APP ectodomain (Zhang et al., 2007). DAPT, also known as N-[N-(3, 5-difluorophenacetyl)-Lalanyl]-S-phenylglycine tert-butyl ester, is a γ-secretase inhibitor (GSI) that inhibits γ-secretase processing of all of its substrates, including APP and Notch, and as a result decreases the levels of all Aβ species. GSM36 is a lead compound developed in our soluble γ-secretase modulator (GSM) program for an AD clinical trial (Wagner et al., 2014). Our GSMs are different from conventional GSIs, displaying high APP selectivity with Notch-sparing properties. They lower Aβ42 levels more preferentially over Aβ40 levels while increasing the levels of shorter Aβ species, e.g., Aβ38, and they do not significantly affect the levels of APP-CTFα/β (Choi et al., 2014; Kounnas et al., 2010; Wagner et al., 2014).

The 7PA2 cells were treated with these various pharmacological agents separately for 24 hrs and harvested. Cell lysates and media were prepared and analyzed by WB using both 6E10 and M3.2. We showed that PMA, DAPT, and GSM36 treatment alone did not lead to Aη-α generation, while ALLN or BACE1i treated samples did (Figure 3A–B). Additionally, ALLN and DAPT remarkably increased CTFβ levels compared to control (Figure 3A–B), as expected and consistent with prior reports (Barthet et al., 2011). Moreover, we found that sAPPα and Aβ proteins were highly expressed and readily detected in the culture medium of 7PA2 cells, which further validated the usefulness of these cells to model the changes of AD-related proteins (Figure 3C–D).We also showed that Aβ levels were robustly decreased by BACE1i and DAPT in cell culture medium compared to control (Figure 3C–D), as expected (Barthet et al., 2011). We also detected proteins with molecular weights slightly higher than Aβ in BACE1i treated medium samples. They may be those modified or oligomerized proteins related to Aβ. Collectively, these pharmacological modulators did not significantly affect η-secretase processing of APP.

Figure 3.

Figure 3

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Effects of pharmacological agents on Aη-α levels and APP processing. Stably-transfected 7PA2 cells were treated with different pharmacological agents for 24 hrs and harvested. Cell lysates and conditioned medium were analyzed by WB. A-B: Cell lysates were subjected to WB for the detection of APP-CTFβ/η using 6E10 (A) or M3.2 antibody (B), respectively. The η-secretase processing products of APP, CTFη and Aη-α, are underlined. C-D: Conditioned medium was subjected to WB and probed with 6E10 (C) or M3.2 (D) antibody, respectively, to detect Aβ and sAPPα.

So far, we showed that the products of η-secretase processing of APP may be regulated by two mechanisms: either through degradation via lysosomal-cathepsin inhibition (Wang et al., 2015) or through generation via BACE1 down-regulation (Willem et al., 2015). We next investigated whether the two mechanisms via ALLN and BACE1i may have a synergistic action toward affecting η-secretase processing of APP. The 7PA2 cells were treated with 10 µM ALLN, or 1 µM BACE1i, or combined ALLN with BACE1i for 24 hrs and then harvested. Cell lysates were analyzed by WB using 6E10 antibody. As shown in Figure 4A, treatment with ALLN or BACE1i alone increased the Aη-α levels, while treatment with ALLN and BACE1i more robustly increased Aη-α levels. We observed a similar dose dependence to this combined treatment as we did for individual treatments (Figure 4B). Taken together, the data clearly showed that cathepsins and BACE1 have synergistic effects in η-secretase processing of APP.

Figure 4.

Figure 4

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ALLN and BACE1i synergistically and robustly increased Aη-α levels. Stably-transfected 7PA2 cells were treated with different pharmacological agents for 24 hrs and harvested. A. Cell lysates were prepared and subjected to WB for the detection of APP processing products through 6E10 antibody. Aη-α levels were increased by 1 µM BACE1i and 10 µM ALLN alone, and more robustly increased by the combined treatment of 1 µM BACE1i and 10 µM ALLN. Short and long exposures (focused on areas around Aη-α) were indicated. B. Cell lysates from cells treated with 10 µM ALLN and different doses of BACE1i (0, 0.125, 0.25, 0.5, and 1 µM) were analyzed by WB for detecting Aη-α levels via the M3.2 antibody. The densitometric measurement for the samples was presented below.

Discussion

The results of this study provide novel and important knowledge toward understanding the mechanisms in regulating APP proteolytic pathway and the pathophysiology of AD. APP undergoes serial proteolytic cleavages to generate pathogenic proteins. It produces Aβ through β- and γ-secretases, or Aη-α through η- and α-secretases. Two recent studies showed that “eta-secretase” processing of APP may be affected through two mechanisms. It may be through either “degradation” via down-regulation of BACE1 or “generation” via inhibition of lysosomal cathepsins. In an effort to validate these two studies, we utilized and compared the two mechanisms in a series of side-by-side experiments. We evidently showed that both mechanisms, indeed, significantly affect η-secretase in processing APP and they have an additive effect on increasing the pathogenic Aη-α protein levels. We found that Aη-α levels were low in untreated cells, but were significantly up-regulated by either BACE1i or ALLN or combined BACE1i and ALLN. Pharmacological modulators of α- and γ-secretase activities did not significantly change η-secretase processing of APP, suggesting their minor roles in η-secretase processing of APP, compared to BACE1i. Furthermore, our results further suggest that increasing CTFη and Aη-α levels due to altered functions of lysosomal cathepsins and BACE1 activity could potentially play a role in AD pathogenesis. Lastly, focusing on η-secretase may provide useful insights into evaluating potential AD therapeutics.

Characterizing the biology of η-secretase in APP processing and generation of the pathogenic Aη-α has benefited from studying the genetics of AD. AD is a genetically complex disease. Four genes have been established to either cause familial early-onset autosomal dominant AD (FAD) with complete penetrance (APP, PSEN1, and PSEN2) or increase susceptibility for lateonset AD with partial penetrance (APOE) (Bertram and Tanzi, 2008). Nearly 200 fully penetrant, autosomal dominant mutations in PSEN1 and PSEN2 have been shown to cause early-onset familial AD (Bertram and Tanzi, 2008). All these genes increase the absolute Aβ levels or the ratios of Aβ42 to Aβ40, which enhance the oligomerization of Aβ into neurotoxic assemblies and formation of amyloid plaque (Bertram and Tanzi, 2008; Gandy, 2005; Hardy and Selkoe, 2002; Tanzi and Bertram, 2005). Amyloid plaque is one essential hallmark of the pathology of AD. While Aβ forms the core of amyloid plaque, an essential hallmark of AD, Aη-α resides in the halo of the amyloid plaque (Willem et al., 2015). Studies using cell models and animal models that harbor FAD mutations showed increased η-secretase activity and elevated pathogenic Aη-α peptides. It would be interesting to study Aη-α in other FAD mutations and physiological roles. Our results warrant future studies focused on effects of Aη-α on the aspects related to amyloid hypothesis, including synaptic dysfunction and neurofibrillary tangle formation, as well as neurodegeneration and plaque formation.

In summary, elucidating the roles of APP proteolytic pathway is critically important in understanding the pathophysiology of AD. Limited knowledge toward APP proteolytic events in generating disease proteins has hindered the development of an effective strategy for an AD therapeutic. The results in this study should advance our understanding of the pathophysiology of AD, as well as provide the knowledge in developing effective strategies for AD treatment or intervention.

Acknowledgments

The authors would like to thank Dr. Steven Rodriguez for the helpful discussions. This work was supported by the grants from the Cure Alzheimer’s Fund (to C.Z. and R.E.T.), Drexel University (to A.J.S.), the Commonwealth of Pennsylvania (to A.J.S.), and National Institutes of Health grants P01AG15379 (to C.Z. and R.E.T.) and R01 NS057295 (to A.J.S.).

Footnotes

Disclosure

The authors report no conflicts of interest.

References

  1. Barthet G, Shioi J, Shao Z, Ren Y, Georgakopoulos A, Robakis NK. Inhibitors of gamma-secretase stabilize the complex and differentially affect processing of amyloid precursor protein and other substrates. FASEB J. 2011;25(9):2937–2946. doi: 10.1096/fj.11-183806. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Bertram L, Tanzi RE. Thirty years of Alzheimer’s disease genetics: the implications of systematic meta-analyses. Nat Rev Neurosci. 2008;9(10):768–778. doi: 10.1038/nrn2494. [DOI] [PubMed] [Google Scholar]
  3. Choi SH, Kim YH, Hebisch M, Sliwinski C, Lee S, D’avanzo C, Chen H, Hooli B, Asselin C, Muffat J, Klee JB, Zhang C, Wainger BJ, Peitz M, Kovacs DM, Woolf CJ, Wagner SL, Tanzi RE, Kim DY. A three-dimensional human neural cell culture model of Alzheimer’s disease. Nature. 2014;515(7526):274–278. doi: 10.1038/nature13800. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Cummings JL. Alzheimer’s disease. N Engl J Med. 2004;351(1):56–67. doi: 10.1056/NEJMra040223. [DOI] [PubMed] [Google Scholar]
  5. Edbauer D, Winkler E, Regula JT, Pesold B, Steiner H, Haass C. Reconstitution of gamma-secretase activity. Nat Cell Biol. 2003;5(5):486–488. doi: 10.1038/ncb960. [DOI] [PubMed] [Google Scholar]
  6. Gandy S. The role of cerebral amyloid beta accumulation in common forms of Alzheimer disease. J Clin Invest. 2005;115(5):1121–1129. doi: 10.1172/JCI25100. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Griciuc A, Serrano-Pozo A, Parrado AR, Lesinski AN, Asselin CN, Mullin K, Hooli B, Choi SH, Hyman BT, Tanzi RE. Alzheimer’s disease risk gene CD33 inhibits microglial uptake of amyloid beta. Neuron. 2013;78(4):631–643. doi: 10.1016/j.neuron.2013.04.014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Hardy J, Selkoe DJ. The amyloid hypothesis of Alzheimer’s disease: progress and problems on the road to therapeutics. Science. 2002;297(5580):353–356. doi: 10.1126/science.1072994. [DOI] [PubMed] [Google Scholar]
  9. Hiltunen M, Lu A, Thomas AV, Romano DM, Kim M, Jones PB, Xie Z, Kounnas MZ, Wagner SL, Berezovska O, Hyman BT, Tesco G, Bertram L, Tanzi RE. Ubiquilin 1 modulates amyloid precursor protein trafficking and Abeta secretion. J Biol Chem. 2006;281(43):32240–32253. doi: 10.1074/jbc.M603106200. [DOI] [PubMed] [Google Scholar]
  10. Kimberly WT, Lavoie MJ, Ostaszewski BL, Ye W, Wolfe MS, Selkoe DJ. Gamma-secretase is a membrane protein complex comprised of presenilin, nicastrin, Aph-1, and Pen-2. Proc Natl Acad Sci USA. 2003;100(11):6382–6387. doi: 10.1073/pnas.1037392100. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Kounnas MZ, Danks AM, Cheng S, Tyree C, Ackerman E, Zhang X, Ahn K, Nguyen P, Comer D, Mao L, Yu C, Pleynet D, Digregorio PJ, Velicelebi G, Stauderman KA, Comer WT, Mobley WC, Li YM, Sisodia SS, Tanzi RE, et al. Modulation of gamma-secretase reduces beta-amyloid deposition in a transgenic mouse model of Alzheimer’s disease. Neuron. 2010;67(5):769–780. doi: 10.1016/j.neuron.2010.08.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Liu K, Doms RW, Lee VM. Glu11 site cleavage and N-terminally truncated A beta production upon BACE overexpression. Biochemistry. 2002;41(9):3128–3136. doi: 10.1021/bi015800g. [DOI] [PubMed] [Google Scholar]
  13. Sisodia SS, St George-Hyslop PH. Gamma-secretase, Notch, Abeta and Alzheimer’s disease: where do the presenilins fit in? Nat Rev Neurosci. 2002;3(4):281–290. doi: 10.1038/nrn785. [DOI] [PubMed] [Google Scholar]
  14. Tanzi RE, Bertram L. Twenty years of the Alzheimer’s disease amyloid hypothesis: a genetic perspective. Cell. 2005;120(4):545–555. doi: 10.1016/j.cell.2005.02.008. [DOI] [PubMed] [Google Scholar]
  15. Vassar R, Bennett BD, Babu-Khan S, Kahn S, Mendiaz EA, Denis P, Teplow DB, Ross S, Amarante P, Loeloff R, Luo Y, Fisher S, Fuller J, Edenson S, Lile J, Jarosinski MA, Biere AL, Curran E, Burgess T, Louis JC, et al. Beta-secretase cleavage of Alzheimer’s amyloid precursor protein by the transmembrane aspartic protease BACE. Science. 1999;286(5440):735–741. doi: 10.1126/science.286.5440.735. [DOI] [PubMed] [Google Scholar]
  16. Veeraraghavalu K, Zhang C, Zhang X, Tanzi RE, Sisodia SS. Age-dependent, non-cell-autonomous deposition of amyloid from synthesis of beta-amyloid by cells other than excitatory neurons. J Neurosci. 2014;34(10):3668–3673. doi: 10.1523/JNEUROSCI.5079-13.2014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Wagner SL, Zhang C, Cheng S, Nguyen P, Zhang X, Rynearson KD, Wang R, Li Y, Sisodia SS, Mobley WC, Tanzi RE. Soluble gamma-secretase modulators selectively inhibit the production of the 42-amino acid amyloid beta peptide variant and augment the production of multiple carboxy-truncated amyloid beta species. Biochemistry. 2014;53(4):702–713. doi: 10.1021/bi401537v. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Walsh DM, Tseng BP, Rydel RE, Podlisny MB, Selkoe DJ. The oligomerization of amyloid beta-protein begins intracellularly in cells derived from human brain. Biochemistry. 2000;39(35):10831–10839. doi: 10.1021/bi001048s. [DOI] [PubMed] [Google Scholar]
  19. Wang H, Sang N, Zhang C, Raghupathi R, Tanzi RE, Saunders A. Cathepsin 1 mediates the degradation of novel APP C-terminal fragments. Biochemistry. 2015;54(18):2806–2816. doi: 10.1021/acs.biochem.5b00329. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Welzel AT, Maggio JE, Shankar GM, Walker DE, Ostaszewski BL, Li S, Klyubin I, Rowan MJ, Seubert P, Walsh DM, Selkoe DJ. Secreted amyloid beta-proteins in a cell culture model include N-terminally extended peptides that impair synaptic plasticity. Biochemistry. 2014;53(24):3908–3921. doi: 10.1021/bi5003053. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Willem M, Tahirovic S, Busche MA, Ovsepian SV, Chafai M, Kootar S, Hornburg D, Evans LD, Moore S, Daria A, Hampel H, Muller V, Giudici C, Nuscher B, Wenninger-Weinzierl A, Kremmer E, Heneka MT, Thal DR, Giedraitis V, Lannfelt L, et al. Eta-secretase processing of APP inhibits neuronal activity in the hippocampus. Nature. 2015;526(7573):443–447. doi: 10.1038/nature14864. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Zhang C. Developing effective therapeutics for Alzheimer’s disease - emerging mechanisms and actions in translational medicine. Discov Med. 2017;23(125):105–111. [PubMed] [Google Scholar]
  23. Zhang C, Browne A, Child D, Divito JR, Stevenson JA, Tanzi RE. Loss of function of ATXN1 increases amyloid beta-protein levels by potentiating beta-secretase processing of beta-amyloid precursor protein. J Biol Chem. 2010a;285(12):8515–8526. doi: 10.1074/jbc.M109.079079. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Zhang C, Browne A, Child D, Tanzi RE. Curcumin decreases amyloid-beta peptide levels by attenuating the maturation of amyloid-beta precursor protein. J Biol Chem. 2010b;285(37):28472–28480. doi: 10.1074/jbc.M110.133520. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Zhang C, Browne A, Divito JR, Stevenson JA, Romano D, Dong Y, Xie Z, Tanzi RE. Amyloid-beta production via cleavage of amyloid-beta protein precursor is modulated by cell density. J Alzheimers Dis. 2010c;22(2):683–984. doi: 10.3233/JAD-2010-100816. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Zhang C, Browne A, Kim DY, Tanzi RE. Familial Alzheimer’s disease mutations in presenilin 1 do not alter levels of the secreted amyloid-beta protein precursor generated by beta-secretase cleavage. Curr Alzheimer Res. 2010d;7(1):21–26. doi: 10.2174/156720510790274428. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Zhang C, Khandelwal PJ, Chakraborty R, Cuellar TL, Sarangi S, Patel SA, Cosentino CP, O’connor M, Lee JC, Tanzi RE, Saunders AJ. An AICD-based functional screen to identify APP metabolism regulators. Mol Neurodegener. 2007;2(1):15. doi: 10.1186/1750-1326-2-15. [DOI] [PMC free article] [PubMed] [Google Scholar]

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