Identifying Genes That Encode APP Metabolism
Modulators On Chromosome 9q22
A thesis
Submitted to the Committee
By Can Zhang
in partial fulfillment of the requirement for the degree
of
Doctor of Philosophy
October 2007 Thesis approval form
2 Dedications
This dissertation is dedicated to my wife, Winnie Hsun-wen Hsu, my parents, Mr. Weiping Zhang and Mrs. Zhenyun Zhang for their tremendous encouragement, support and love.
3 Acknowledgments
In retrospect as I approach the completion of my doctorate, I feel a deep gratitude towards so many people for their incredible assistance and generous support. Without their help I could not be where I am here today. I sincerely would like to express my gratitude to each of them, although it would be impossible for me to name all.
First of all, I would like to express my sincerest and most genuine gratitude to my graduate advisor, Dr. Aleister Saunders. Dr. Saunders is a great motivator and inspirer and I have benefited tremendously from his knowledge, vision and enthusiasm. I feel most indebted to him for leading me from intellectual poverty to flourish, and getting close toward freedom. I am grateful to him for spending tremendous time and effort leading, supporting and encouraging me for the past 5 years. I feel greatly thankful for his suggestions and recommendations for my future career. Without his help and effort, it would be impossible for me to even get close to this point.
In particular, I want to sincerely thank all my committee members during my Ph.D. education and training, Dr. Mary K Howett, Dr. Irwen Chaiken, Dr.
Brian Balin, Dr. Mark Lechner, Dr. Joe Bentz and Dr. Dan Marend, for the tremendous input of their valuable time and suggestions. I feel greatly thankful to Dr. Jeremy Lee for his valuable expertise and support on my research.
Many thanks are due to many of my collaborators. I specially thank Dr.
Rudi Tanzi, Dr. Lars Bertram, Dr. Mikko Hiltunen, Dr. Rob Moir, and Dr.
Zhongcong Xie for their valuable suggestion and assistance. I would thank Mr.
Jeff Thomas and Dr. Greg Johannes for their assistance on pulse-chase
4 experiments. I would also thank Dr. Dan Marenda for his support and work on characterization of drosophila.
I am greatly thankful for Ms. He Zhao and Dr. Bahrad A. Sokhansanj for generously providing experimental materials and assistance. Thanks are also due to Mr. Joseph Owusu-Boateng for his experimental assistance on single cell electrophoresis. I thank all the faculty members that I had the opportunity to take courses from or be their teaching assistant. I thank current and previous graduate and undergraduate students from Dr. Aleister Saunders’ laboratory, particularly Preeti Khandelwal, Ranjita Mukherjee, Neha Patel and Sara
Ansaloni, for the opportunity of working together and their valuable assistance.
Special thanks are due to my former M.S and M.D. education professors and teachers from Weifang Medical College, China, particularly Mr. Zailian Li, for their guidance and support. I feel sincerely thankful to Dr. Steve Jun Hou and his family for their assistance on my getting use to U.S. life.
Many thanks are due to my colleagues and friends in Drexel who made my life here memorable. I thank the Drexel BBGSA (Bioscience and
Biotechnology Graduate Student Association) for organizing all the activities. I would like to thank the entire faculty, staff and students in my department for making the Department such a joyful working and studying environment.
Especially, I am greatly grateful to my wife, Winnie Hsu, for her everyday encouragement and love for me. I am tremendously indebted to my parents and relatives, particularly my parents, Mr. Weiping Zhang and Mrs. Zhenyun
Zhang, whose persistent encouragement and unconditional love enabled me to finish this work.
5 TABLE OF CONTENTS
ABSTRACTS ………………………………………………………………………...10
CHAPTER 1: Background……………………………………………………….....12
1.1 Introduction of Alzheimer’s disease ………………………………………….12
1.2 Therapeutic targeting of the α-secretase pathway to treat Alzheimer’s
disease……………………………………………………………………………26
CHAPTER 2: An AICD-based Functional Screen to Identify APP Metabolism
Regulators ……………………………………………………………………….…..36
CHAPTER 3: Functional characterization of APP metabolism regulators …..101
3.1 Ubiquilin 1 and Protein Quality Control... …………………………………..101
3.2 Characterization of Ubiquilin 1 functions in APP metabolism and its
interaction with the proteasome system……………………...…...... 114
CHAPTER 4: Conclusion and future directions…………………………...... 144
LIST OF REFERENCES…………………………………………………...... 158
VITA………………………………………………………………………………….172
6 LIST OF TABLES
1-1 Known genes that modulate AD risk………………………………………19
2-1 Z-factor values ………………………………………………………………76
2-2 List of genes that their encoded proteins down-regulation decreases AICD- medicated luciferase and are considered positive APP metabolism regulators………………………………………………………99
2-3 List of genes that their encoding proteins down-regulation increases AICD-medicated luciferase and are considered negative APP metabolism regulators. …………………………………………………….100
4-1 APP metabolism regulators identified utilizing the AICD-mediated luciferase assay combining table 2-2 & 2-3 ……………………………..156
7
LIST OF FIGURES
1-1 Amino acid sequence of Aβ in APP695 and the cleavage site of α-, β-, and γ-secretases. ………………………………………………………….14
1-2 Schematic overview of APP processing by the α-, β-, and γ- secretases…..………………………………………………………………14
2-1A Schematic overview of APP processing by the α-, β-, and γ- secretases…………………………………………………………………...30
2-1 Functional screen for regulators of APP metabolism…………………..77
2-2 Pharmacological modulation of secretases alters AICD-Gal4 levels and AICD-Gal4 mediated luciferase activity in SY5Y-APP-Gal4 cells………………………………………………………………………….78
2-3 Over-expression of individual secretase genes in SY5Y-APP-Gal4 cells increases AICD-Gal4 mediated luciferase activity………………..81
2-4 Knock-down of APP and individual secretase genes in SY5Y-APP- GAL4 cells decreases AICD-Gal4 mediated luciferase activity……….84
2-5 Genetic alteration of Fe65 or Tip60 levels modulates AICD-Gal4 mediated luciferase activity……………………………………………….87
2-6 Ubiquilin 1 knock-down regulates APP-Gal4 metabolism in SY5Y- APP-Gal4 cells……………………………………………………………..90
2-7 Ubiquilin 1 over-expression regulates APP-Gal4 metabolism in SY5Y-APP-Gal4 cells……………………………………………………...92
2-8 Quantification of Western blot densitometries from Figures 6 & 7…….93
2-9 Correlation between AICD-Gal4 mediated luciferase levels and AICD-Gal4 levels determined by Western blot analysis………………..94
2-10 APP-Gal4 fusion protein architecture………………………...... 95
2-11 APP-Gal4 fusion protein amino acids sequence………………………..96
2-12 Screening of APP metabolism regulator using an AICD-mediated functional assay…………………………………………………………....97
2-13 Eleven genes were identified as APP metabolism regulators using our validated AICD-mediated functional assay………………………………98
3-1A Protein quality control systems in mammalian cells…………………..106
8
3-1 Uiquilin 1 protein level is robustly knocked down…………………..….132
3-2 Ubiquilin 1 knock-down does not change APP half life, but changes mature and immature ratio…………………………………………….…133
3-3 Ubiquilin 1 knockdown decreased cell surface APP……………….….135
3-4 Ubiquilin 1 effect on APP metabolism modulation is dependent on proteasome system………………………………………………………136
3-5 Down-regulation of Ubiquilin1 decreases cell viability and elevates caspase -3 activity…………………………………………………………138
3-6 Ubiquilin 1 degradation requires proteasome and lysosome………...140
3-7 Ubiquilin 1 decreases Aβ levels, as well as Aβ42/Aβ40 ratio………...141
3-8 γ-secretase inhibition increases Ubiquilin 1 protein levels…………....142
4-1 Route of protein trafficking……………………………………………….147
4-2 APP synthesis and degradation…………………………………………148
9
Abstract
Identifying genes that encode APP metabolism modulators on chromosome 9q22
Can Zhang Advisor: Aleister Saunders, Ph.D.
The central pathobiologic event in AD is regulated intramembrane proteolysis of the β-amyloid precursor protein (APP), which generates the amyloidogenic β-amyloid (Aβ) peptide and the APP intracellular domain (AICD).
The AICD fragment displays transcriptional activation properties. The genetic cause of AD is not fully accounted for by the known risk genes, and recent meta-analysis suggests that there are around 20 unidentified genes.
Chromosome 9q22 is implicated to harbor AD genes by human genome scan results. Here I constructed an AICD-mediated functional assay to discover novel APP metabolism regulators. 11 positional candidate genes have been identified on this region and their functions are undergoing further characterization. I found that Ubiquilin 1 modulates APP metabolism and this function is cell type dependent. I also found that Ubiquilin 1 can modulate cell viability and its degradation requires proteasome and lysosome.
10 This page is left blank on purpose.
11
CHAPTER 1
Background
CHAPTER1.1
Introduction of Alzheimer’s disease
AD, APP and Amyloid Hypothesis
Alzheimer’s disease (AD) is a progressive and degenerative disorder clinically characterized by progressive dementia that inevitably leads to incapacitation and death. AD is the most common form of dementia and affects
40% individuals aged over 85. The majority of AD cases are late onset (>65 years), this form is genetically complex[1]. The minority of AD cases (5-10%) are early-onset familial AD (FAD) with an autosomal dominant inheritance pattern[2, 3]. There are no effective therapies currently. Upon autopsy, massive synaptic loss and neuronal death is observed in brain regions critical for cognitive function, including cerebral cortex and hippocampus. There are two hallmarks that define AD at the microscopic level: (1) amyloid plaques, extracellular deposits primarily composed of the 4 kDa, 39–43 amino acid Aβ peptide[4-8], and (2) neurofibrillary tangles, intracellular aggregates of the microtubule associated protein tau[9-11]. The Aβ peptide is generated from an integral type I membrane protein β-amyloid precursor protein (APP)[12-15].
12 Evidences from genetic and biochemical studies support the amyloid cascade hypothesis of AD which states that accumulation and aggregation of Aβ is the primary cause of AD. Aβ accumulation induces an inflammatory response followed by neuritic injury, hyperphosphorylation of tau protein and formation of fibrillary tangles, leading ultimately to neuronal dysfunction and cell death [16-
19]. Animal models which reproduce AD pathology and develop amyloid deposits show learning deficits reminiscent of those of humans affected with the disease. Animal models studies that decrease neuronal Aβ burden using passive or active immunization can also ameliorate learning deficits of affected animals [20-22]. Thus, prevention of Aβ production and accumulation is currently being evaluated as a potential therapeutic intervention for AD.
α-, β-, And γ-secretases.
Aβ peptide is proteolytically cleaved from APP by several different proteases called α-, β-, and γ- secretase. Figure 1-1 and 1-2 are the schematic overview of APP processing by the α-, β-, and γ-secretases. This will be also described in my first review paper. Figure 1-1 shows the amino acid sequence of β amyloid precursor protein (APP) upstream of the transmembrane segment
1 (underlined, bold) and encompassing the sequences of Aβ 1-40 and Aβ 1-42 (D -
V40 and D1- A42, respectively). The β -secretase cleaves at D1. The -secretase cleaves at Lys16, and the γ-secretase cleaves at Val40 and/or Ala42. Aβ (1-40) is soluble, which accounts for approximately 90% of the total Aβ secreted. Aβ (1-
42) is insoluble, and it accounts for approximately 10% of the total Aβ secreted.
However, Aβ (1-42) constitutes the major component of the nonfibrillar extracellular plaques that precede the development of the dense, fibrillar
13 neuritic plaques characteristic of AD[23]. The left side of Figure 1-2 represents the nonamyloidogenic pathway in which sAPPα and C83 are generated.
Subsequent hydrolysis by the γ-secretase produces a p3 peptide and APP intracellular domain (AICD). The right side of Figure 1-2 represents the amyloidogenic pathway in which sAPPβ and C99 are liberated. Subsequent hydrolysis by the γ-secretase releases Aβ found in plaque deposits and AICD.
Proteolysis by γ-secretase is heterogeneous, which produces different length
Aβ species, with longer and more hydrophobic species more prone to fibril formation.
Figure 1-1 Amino acid sequence of Aβ in APP695 and the cleavage site of α-, β-, and γ-secretases.
14 Figure 1-2 Schematic overview of APP processing by the α-, β-, and γ- secretases.
Several proteins from the adamalysin family have α-secretase activities, and they are tumour necrosis factor-alpha convertase (TACE, or ADAM17),
ADAM10, and ADAM9[24-27]. ADAMs (A Disintegrin And Metalloprotease domain) are multifunctional, membrane-bound cell surface glycoproteins, which have been implicated in cell adhesion, protein ectodomain shedding, matrix protein degradation and cell fusion. [28-32];[33, 34]. Some ADAMs have a consensus zinc-binding motif, HEXXH, in the catalytic domain. In addition to cleaving APP, ADAM 10 and ADAM 17 are both implicated in the ectodomain shedding of various cell surface molecules including the Notch, the IL6-receptor and the transmembrane chemokines CX3CL1 and CXCL16. These molecules are constitutively released from cultured cells, a process that can be rapidly induced by cell stimulation with phorbol esters such as PMA (phorbol 12- myristate 13-acetate)[35]. Recent research supports the view that the constitutive cleavage predominantly involves ADAM10 while the inducible one is mediated to a large extent by ADAM17. Mice with ADAM17ΔZn/ΔZn null mutation died at birth with phenotypic changes, including failure of eyelid fusion, hair and skin defects, and abnormalities of lung development[36]. TAPI-1 (TNF-
α protease inhibitor -1) blocks cleavage of cell surface TNF and inhibits constitutive sAPPα release [37]. TAPI-2 and 1,10-phenanthroline, which are known to inhibit metalloproteases, block PMA-activated shedding of proTGF-α, cell adhesion receptor L-selectin, interleukin 6 (IL-6) receptor α subunit, and
APP[38].
15 β-site APP cleaving enzyme (or β-secretase, BACE, BACE1, Asp2 or memapsin 2) is a member of the pepsin family of aspartyl proteases [39-44]. It is a type 1 transmembrane protein, which has an N-terminal catalytic domain, containing two catalytic aspartic residues, linked to a 17-residue transmembrane domain and a short C-terminal cytoplasmic tail. Within the cell,
BACE is expressed initially as a preproprotein, and then efficiently processed to its mature form in the Golgi. To date, no mutations in the BACE gene have been identified that strongly associate with AD. BACE activity is present in the majority of cells and tissues, while maximal activity is found in neural tissue and neuronal cell lines. BACE has maximal activity at acidic pH, and its activity is highest in the acidic subcellular compartments of the secretary pathway, including the Golgi apparatus and endosomes. With the discovery of BACE, another human homologue was identified and called BACE2. BACE2 is not highly expressed in the brain, and the lack of any detectable Aβ in BACE−/− animals suggests that BACE2 does not contribute to the generation of Aβ [45].
The studies of BACE knockout mice showed that β-secretase activity was abolished in brains and cultured neurons of BACE−/− mice [46, 47]. All the knockout strategies produce viable, fertile BACE deficient (BACE−/−) mice, which appear to develop normally and have no discernable abnormalities compared to BACE+/+ mice. Moreover, investigations of gross behavioral and neuromuscular parameters of BACE−/− mice demonstrate no obvious differences with wild-type mice. And transgenic BACE expression in mouse neurons can accelerate amyloid plaque pathology [48]. With the discovery of
BACE, another human homologue was identified and called BACE2 ([49]).
BACE2 is not highly expressed in the brain, and the lack of any detectable Aβ
16 in BACE−/− animals suggests that BACE2 does not contribute to the generation of Aβ [45].
γ-secretase cleaves APP using a novel proteolytic mechanism, termed regulated intramembrane proteolysis (RIP) [50]. Four membrane proteins are now well known to be members of γ-secretase complex: presenilin (PS1), presenilin enhancer-2 (PSEN2), nicastrin (NCSTN) and anterior pharynx defective-1 (APH-1)[51, 52]. Recently CD147 is found to exist as a regulatory subunit of the γ-secretase complex, and it down-regulates Aβ production. [53].
Evidence suggests that PS (PS1 and PS2) are aspartyl proteases that are active in RIP. A number of different functions have been ascribed to PS, including a role in promoting or reducing the susceptibility of neurons to apoptosis [54] and regulating intracellular calcium signaling and calcium- mediated apoptosis [55, 56]. The well substantiated functions for PS are γ- secretase cleavage of APP and Notch [57, 58]. The Notch protein functions as a receptor at the cell surface and mediates cell cell signaling interactions to specify cell fates within an equivalence group, a role that is particularly important during development. Notch is activated by a proteolytic cascade similar to that of APP. The PS1/2 double homozygous deficient knockout (KO) mouse displays a number of severe phenotypes that are similar to the
Notch1−/− mouse [59]. A role for PS in APP metabolism was suggested from observations that PS familial AD (FAD) mutations cause an increase in the ratio of Aβ42: Aβ42+40, indicating altered processing at the γ-secretase cleavage site. PS FAD overexpression assays in tissue culture systems confirmed the
FAD increase in the ratio of Aβ42: Aβ42+40 [60]. Transgenic mice that express mutant PS transgenes display elevated brain Aβ levels but do not develop
17 neuritic plaques [61]. However, mice that overexpress both a PS1 FAD mutant and the APPsw mutant (APP Swedish mutation KM595/596NL) show accelerated Aβ deposition compared with mice expressing the APPsw mutant alone). PS1-deficient mice (PS1−/−) have retarded embryonic growth and die shortly before or immediately after birth [62]. Re-introduction of wild-type (wt)- hPS1 or FAD-hPS1 equivalently rescues the PS1−/− mice from embryonic lethality (Davis et al. 1998; Qian et al. 1998). Unlike PS1−/− mice, PS2−/− mice are viable, fertile and have no detectable abnormalities in APP metabolism.
The only phenotype identified in the PS2−/− mouse was the development of mild pulmonary fibrosis and hemorrhage as the animals aged [59]. These results directly implicate PS as the γ-secretase enzyme or an essential co-factor for γ- secretase cleavage of APP C99/C83. L-685,458 is a novel, potent and selective cell-permeable γ-secretase inhibitor[63-65]. It exhibits over 100-fold greater selectivity for γ-secretase than for a panel of other proteases. [63]
And it potently inhibits γ -secretase and thus the production of Aβ total (IC50 17 nM), Aβ (1-40), (IC50 48 nM) and Aβ (1-42) (IC50 67nM) in human neuroblastoma SH-SY5Y cells overexpressing spbA4CTF, a truncated form of human APP.
In addition to generating Aβ peptide, γ- secretase cleavage of APP produce APP intracellular domain (AICD). AICD forms a transcriptionally active complex containing the nuclear adaptor protein Fe65 and the histone deacetyltransferase TIP60 [66]. This complex targets, for example, the KAI1 promoter. Ehrmann and Clausen reported a genetic system to monitor and investigate γ-secretase function and assembly [67]. The system involves
18 coexpression of γ- secretase and a APP C-terminal fragment (CTF) of 55 residues, including the transmembrane segment, fused to the Gal4 transcriptional activator. Proteolytic processing of the reporter protein releases the Gal4 domain from the membrane from where it translocates to the nucleus and activates transcription of the promoter that drives expression of the reporter gene lacZ. lacZ, encoding β-galactosidase, provides a quantitative output signal that correlates with the activity of γ-secretase. This system might allow mutational approaches to study such important questions as substrate specificity and the identification of processes that regulate γ-secretase activity.
For example, random mutagenesis of genes encoding γ- secretase components could be used to identify which components are involved in excluding full-length APP from processing.
AD Genes Genetic Chromo- Biological Type Mechanism some Mechanism APP 13 21 Early PSEN1 150 14 APP metabolism Onset PSEN2 7 1
Late APOE ε4 19 APP metabolism Onset
Table 1-1: Known genes that modulate AD risk. Early onset AD can be caused by any of over 150 mutations in three known genes (APP, PSEN1, and PSEN2). A common polymorphism in the gene encoding apolipoprotein E
(APOE) confers increased risk for late-onset AD. Human AD genome scan demonstrated multiple chromosomes harbor AD putative genes. Their expressed proteins may interact with each other, and may influence Aβ degradation and clearance.
19 Genetic studies of AD
There are two forms of AD: early onset and late onset (Table 1-1). Early onset can be caused by any one of over 150 mutations in three known genes
[1]: the amyloid precursor protein (APP), presenilin 1 (PSEN1) and presenilin 2
(PSEN2). A common polymorphism ( 4) in the gene encoding apolipoprotein E
(APOE) confers increased risk for late-onset AD [68], and lowers the age of onset in a dose-dependent fashion [69, 70]. There are more than 100 high risk genes reported for late onset, but they were not universally confirmed mostly due to the failure of replication. Genetic and epidemiological studies have shown that genes beyond APOE are involved in disease etiology. Simulation studies estimated that up to 4 additional major genes as well as several minor
AD genes remain to be identified [71, 72]. Kehoe et al have genotyped 292 affected sibling pairs (ASPs) with AD with onset ages of >/=65 years using 237 microsatellite markers separated by an average distance of 16.3 cM[73]. They found the highest lod scores on chromosomes 1, 9, 10 and 19. Myers et al performed a two-stage genome screen to search for novel risk factors for late- onset Alzheimer disease (AD) [74]. They found 10 peaks on chromosomes 1
(peak B), 5, 6, 9 (peaks A and B), 10, 12, 19, 21, and X. In 2003, Blacker et al performed a 9cM genome screen of 437 families with AD[75] from the full
National Institute of Mental Health (NIMH) sample, which had been carefully ascertained, evaluated and followed over the last decade. They observed a
'highly significant' linkage peak on chromosome 19q13, which probably represents APOE. Twelve additional locations (on 1q23, 3p26, 4q32, 5p14,
6p21, 6q27, 9q22, 10q24, 11q25, 14q22, 15q26 and 21q22) met criteria for
20 'suggestive' linkage. While some of the more marginal peaks in the study probably represent false-positive findings, others, particularly those with relatively stronger signals and/or prior reports (e.g. on chromosomes 9q22), are more likely to harbor genuine AD susceptibility genes.
RNAi
Recently, gene silencing by RNA interference (RNAi) in mammalian cells using small interfering RNAs (siRNAs) and short hairpin RNAs (shRNAs) has become a valuable genetic tool. [76-78] [79]; [80]; [81]; [82]; [83]; [84]; [85];[86].
The phenomenon of RNA interference (RNAi) was first described by Fire et al. eight years ago[87]. They observed that the response to double-stranded RNA
(dsRNA) in the nematode, Caenorhabditis elegans, resulted in potent sequence-specific gene silencing at the post-transcriptional level. The complete spectrum of biological processes in which the RNAi machinery acts is far from clear. However, it has been suggested that the RNAi is an RNA-based cellular
‘immune system', and is an evolutionarily conserved mechanism for combating viruses or parasitic endogenous genetic elements. Mechanistically, RNAi is a two-step process. In the first step, the dsRNA that triggers the silencing response is cleaved into small interfering RNAs (siRNAs) [88] of 21–23 nucleotides. This is accomplished by Dicer, an RNase-III-family nuclease. In the second step, siRNAs are incorporated into a targeting complex, known as
RISC (RNA-induced silencing complex), which destroys mRNAs that are homologous to the integral siRNA [89]. The RNAi gene knockdown technique is developed after discovering microRNA (miRNA) features. MiRNAs are small noncoding RNA gene products about 22 nt long that are processed by Dicer
21 from precursors with a characteristic hairpin secondary structure [90-93]. miRNAs are produced by Dicer from the precursors of approximately 70 nucleotides (pre-miRNAs), these clustered miRNAs are expressed polycistronically and are processed through at least two sequential steps: (i) generation of the approximately 70 nucleotide pre-miRNAs from the longer transcripts (termed pri-miRNAs); and (ii) processing of pre-miRNAs into mature miRNAs. Subcellular localization studies showed that the first and second steps are compartmentalized into the nucleus and cytoplasm, respectively, and that the pre-miRNA serves as the substrate for nuclear export. Lee’s study (Lee et al. 2002) suggested that the regulation of miRNA expression may occur at multiple levels, including the two processing steps and the nuclear export step.
Over a hundred miRNA genes have been discovered so far from biochemical and bioinformatic studies of Caenorhabditis elegans, Drosophila melanogaster and Homo sapiens.
siRNA is 21 bp in length and is essentially chemically synthesized RNAs that mimics of Dicer cleavage products. These small RNAs have been shown to induce sequence-specific gene silencing when transiently transfected into mammalian cells. shRNA uses expression constructs harboring a 19–29 bp inverted repeat that forms a short hairpin when transcribed in vivo. It reproduces the secondary structure of endogenous interfering RNAs (micro
RNAs). The short hairpin RNA (shRNA) constructs are expressed as human microRNA-30 (mir-30) precursors (Paddison et al. 2004). The mir-30-styled shRNA is synthesized as a single stranded DNA oligo with common ends corresponding to part of the endogenous mir-30 miRNA flanking sequence.
22 These common sequences are used to prime a PCR reaction, whereby the entire mir-30-styled shRNA is amplified to produce a clonable PCR product.
Significance
Alzheimer's disease (AD) is a devastating neurodegenerative disorder of late life with complex inheritance. The molecular mechanisms of are is still not complete understood; there are still up to 4 genes to be identified. And there are no effective therapies that can modify the process the disease. Our research is aimed at utilizing an AICD mediated reporter gene assay system to identify novel modulators of APP processing on chromosome 9q22. The substantial effort required to identify and characterize as many of AD risk genes as possible will help us better understand APP processing pathway, and will greatly facilitate the development of strategies for treatment, early intervention, and prevention of this devastating disease.
Specific Aims
Alzheimer’s disease (AD) is a progressive and degenerative disorder, which is clinically characterized by progressive dementia that inevitably leads to incapacitation and death. The central pathobiologic event in AD is the regulated intramembrane proteolysis (RIP) of the β-amyloid precursor protein (APP), which generates the amyloidogenic β-amyloid (Aβ) peptide and the transcriptionally active APP intracellular domain (AICD). Excessive Aβ accumulation in the neurons is believed to be the primary cause of the disease.
The genetic cause of AD is not fully accounted for by the known risk genes, and studies show there are around 4-7 other genes that contribute to the disease [72]. Five independent genome scan showed more than 10
23 chromosome regions may contain AD candidate genes. Chromosome 9q22
(75Mb to 110Mb) is implicated in three of the five genome scans[73-75], and has constantly high LOD scores. Since APP processing is the central event of
AD, AD risk genes may play an important role in APP processing. This proposal is aimed at identifying novel modulators of APP processing on chromosome 9q22, a region that is suggested to contain AD risk genes.
Identifying novel modulators will increase our understanding of APP processing, and illuminate new avenues for therapeutic intervention. There are five specific aims in this research.
1. Constructing and validating the AICD - mediated luciferase reporter gene assay system. In the APP differential processing pathway, APP is cleaved by α or β- secretase, which produces differential N- and C- termini. Then the C- terminus products are further cleaved by γ- secretase, which releases Aβ, P3 and AICD. Since AICD can enter nucleus and activate gene expression, we will construct a cell based assay in which luciferase gene expression level is regulated by AICD levels. We will validate that luciferase gene expression is regulated in concordance with known APP processing pathways. α-, β-, and γ- secretase inhibitors and stimulators which regulate APP proteolytic pathway will be used to validate this assay system. Once validated, this assay system will be used to identify novel modulators of APP processing.
2. Establishing shRNA technology. The technology of short hairpin RNAs
(shRNAs) has become a valuable genetic tool, which allows us to inhibit specific genes and study their functions in mammalian cells[76, 94-96]. Now a shRNA expression library targeting human genome has been established and
24 constructed into retrovirus packaging vectors. These shRNA expression constructed are based on plasmid and contain puromycin resistance. We will transfect them into our assay cells and select with puromycin. Transfection efficiency is a crucial issue of the shRNA effects; we will compare available transfection reagents, and will optimize the one that have the highest transfection efficiency.
3. Identifying novel modulators of APP processing. After we have validated our AICD mediated luciferase gene assay system and established shRNA technology in our laboratory, we will knock down positional candidate genes on chromosome 9q22 in our assay system. The choice of chromosome
9q22 is from human AD genome screens which suggest that this region harbors
AD high risk genes. Luciferase activity will be monitored. The shRNAs that modulate luciferase activity may be involved in APP processing, and their genes should be considered putative AD genes. To confirm the putative AD genes can modulate APP processing, overexpression of those genes will be carried out.
4. Characterize mechanisms by which novel modulators alter APP processing. The modulaotrs may influence APP processing by affecting α-, β-, and γ- secretases, and other mechanisms as well. To study how the novel regulators modulate APP processing, we will measure substrates and products that are involved in APP processing. We will examine α-, β-, and γ- secretases level alteration. Characterization of APP processing by those modulators may illuminate new avenues for therapeutic intervention.
25
CHAPTER 1.2 Therapeutic targeting of the α-secretase pathway to treat Alzheimer’s disease
Can Zhanga, Aleister J. Saundersa,b
a- Department of Bioscience & Biotechnology, Drexel University, Philadelphia, PA b- Department of Biochemistry & Molecular Biology, Drexel University College of Medicine, Philadelphia, PA
Alzheimer’s disease (AD) is a progressive and degenerative disorder pathophysiologically characterized by beta-amyloid peptides (Aβ) accumulation in the brain. Aβ is indicated to be the primary agent in the pathogenesis of AD. Aβ is generated from the amyloid precursor protein (APP) via two proteolytic activities, β-, and γ- secretases. α-Secretase cleavage is an alternative proteolytic cleavage which prevents Aβ production and deposition. Elevating α-secretase activity, therefore, is a potential therapeutic strategy to treat AD.
Introduction Alzheimer’s disease (AD) is a progressive and degenerative disorder clinically characterized by dementia that inevitably leads to incapacitation and death. It was named after Dr. Alois Alzheimer by colleagues after he first described the disorder in 1907. AD is the most common form of dementia and affects about 40% individuals aged over 85. More than 5 million Americans now have AD. There is currently no cure to stop disease progression. 5-10% AD cases are classified as early-onset AD, defined by an age of onset of 60 or less.
More than 160 mutations in three genes (APP, PSEN-1, and PSEN-2) have
26 been identified that cause this form of disease [1]. These mutations are 100% penetrant which means any individual mutation will definitely cause early onset of AD. Given this strong genetic cause, this form of the disease is often referred to as familiar AD. On the other hand, 90-95% of AD cases are late onset (>65 years). Advanced age and a family history of the disease are the largest risk factor for this form of AD. Genetically, common polymorphisms increase the risk of developing AD. Polymorphisms in one gene, APOE, have been repeatedly confirmed to modulate AD risk. Specifically the APOEε4 allele increases AD risk. Recent meta-analysis results suggest that AD risk is modulated by numerous genes (~25), each displaying a small yet significant effect on risk. In addition to age and genetics, environmental factors such as diet, smoking, education also modulate risk of developing late onset AD.
The underlying mechanism of AD pathogenesis is still not completely understood. At the microscopic level, AD is characterized by two obvious pathological hallmarks: (1) amyloid plaques, primarily composed of the 39–43 amino acids Aβ peptide; and (2) neurofibrillary tangles, paired helical filaments of hyperphosporylated Tau protein. A large amount of genetic, cell biology and biochemical evidences support the amyloid cascade hypothesis of AD pathogenesis. This hypothesis states that Aβ production and accumulation is the primary cause of AD. Soluble Aβ affects synaptic functions. Accumulated insoluble Aβ induces an inflammatory response followed by neuritic injury, hyperphosphorylation of tau protein and formation of fibrillary tangles, leading ultimately to neuronal dysfunction and cell death[1, 16, 97]. The Aβ peptide is generated from an integral type I membrane protein β-amyloid precursor protein (APP). Thus, genes and proteins that can modulate APP metabolism
27 and decrease Aβ production and deposition are currently envisioned as good targets for AD therapeutics.
APP metabolism- amyloidogenic and non-amyloidogenic pathways
APP is a transmembrane protein with a long extracellular or luminal domain, a transmembrane domain and a short cytoplasmic domain (Figure 1-1
& 1-2). APP gene is localized on chromosome 21. APP mRNA undergoes alternative splicing to yield eight possible isoforms, three of the APP isoforms
(the 695, 751 and 770 amino acid isoforms) predominate in the brain. APP695 is produced mainly in neurons; APP751 and APP770 are found mostly in non- neuronal glial cells.
APP can undergo sequential proteolytic cleavage (Figure 1-1 & 1-2).
Depending on whether Aβ is generated, the pathway is termed amyloidogenic or non-amyloidogenic. In the amyloidogenic pathway, the protease that first cleaves APP is β-secretase. This extracellular cleavage yields soluble N- terminal APP (or sAPPβ) and a transmembrance C-terminal fragment (β-CTF).
β-CTF is then cleaved within the membrane by γ-secretase and generates Aβ and APP intracellular domain (AICD). Depending on the γ-secretase cleavage site, there are two main Aβ species generated: Aβ40 and Aβ42. In the physiological state, there are much more Aβ40 than Aβ42. However in amyloid plaques, more Aβ42 is found than Aβ40. β-secretase is encoded by a gene named BACE or BACE1, and is a member of the pepsin family. Like APP,
BACE is also a transmembrane protein, which has an extracellular N-terminal domain, containing two catalytic aspartic residues, linked to a transmembrane domain and a short cytoplasmic C-terminal domain. BACE2, a BACE1 homologue, has both β-secretase and α-secreatase activities. γ-secretase is a
28 member protein complex, which contains five membrane proteins: presenilin
(PSEN), presenilin enhancer-2 (PEN2), nicastrin (NCSTN), APH-1 and CD147.
In the non-amyloidogenic pathway, the protease that first cleaves APP is
α -secretase. This cleavage occurs in the middle of the Aβ region and yields soluble N-terminal APPα (sAPPα) and a transmembrane C-terminal fragment
(α-CTF). sAPPα has neurotrophic and neuroprotective functions. Similar to β-
CTF, α-CTF is then cleaved by γ-secretase to generate P3, instead of Aβ, and
AICD. Three enzymes have been shown to harbor α-secretase activity: ADAM9,
ADAM10 and ADAM17 (also named as tumour necrosis factor-α convertase or
TACE). They all belong to the family of ADAMs (A Disintegrin And
Metalloprotease domain), which are multifunctional, membrane-bound cell surface glycoproteins, which have been implicated in cell adhesion, protein ectodomain shedding, matrix protein degradation and cell fusion.
The currently approved medicine in the U.S. only treats the symptoms, particularly cognitive deficit, but not the underlying cause of AD, APP proteolysis and Aβ accumulation. Four drugs, Tacrine (Cognex), donepezil
(Aricept), galantamine (Reminyl), and rivastigmine (Exelon), belong to cholinesterase inhibitors that can suppress the enzymatic hydrolysis of neurotransmitter acetylcholine, thus maintain a higher acetylcholine concentration in the neuronal synapse [98]. Another drug, Memantien (Axura), is a moderate affinity NMDA-receptor antagonist.
29
Figure 2-1A Schematic overview of APP processing by the α-, β-, and γ- secretases.
Inhibition of amyloidogenic processing is an obvious therapeutic approach to treating AD. Indeed, compounds that inhibit β- or γ-secretase are in clinical trials currently. Other treatments that target Aβ production and accumulation are also under active investigation, and include α-secretase stimulation, immunotherapy, selective Aβ42-lowering agents (tarenflurbil), inhibitors of amyloid aggregation (tramiprosate), and cholesterol-lowering agent
(statins). Some of them have reached phase III clinical trials and some trials have failed due to the safety and adverse actions. Among these strategies, stimulating α-secretase is the only direct gain-of-function strategy and appears to be the safest strategy because sAPPα is indicated to be neurotrophic and neuroprotective. Our group and some other groups showed that ADAM9,
ADAM10 and ADAM17 are constitutively expressed; in addition, the activities of
30 ADAM10 and ADAM17 can be stimulated by a number of signal transduction pathways including protein kinase C (PKC), phospholipase A2 (PLA2) and muscarinic acetylcholine receptors (mAChRs). Therefore genes and pathways that stimulate α-secretase activities should be considered good therapeutic targets. The rest of this review will discuss the current progress of identifying genes that can modulate α-secretase activity and the progress characterizing
α-secretase stimulators.
Genes and proteins that can modulate α- secretases activity
The results of genetic linkage and association studies have been very useful in identifying the genes, proteins, pathways involved in AD pathogenesis.
These approaches have also identified α-secretase modulators.
N-arginine dibasic convertase (NRDc or Nardilysin) is a peptidase which hydrolyses peptide substrates on the N-terminus of arginine residues. The gene Nardilysin is located on chromosome 1p32, a genomic region implicated to harbor an AD locus from linkage studies[75]. The nardilysin protein enhances the α-secretase activity directly by regulating ADAMs and decreases the generation of Aβ using unidentified mechanism. The nardilysin protein is expressed in adult heart, skeletal muscle, testis and cortical neurons of the human brain.
CYP46A1 (cytochrome P450, family 46, subfamily a, polypeptide 1) is another gene whose encoded protein can elevate α-secretase activity indirectly.
The gene is located on the chromosome 12 in a region linked to AD, and polymorphisms within the gene have been associated with increased AD risk.
CYP46A1 encodes the protein cholesterol 24- hydroxylase, which is expressed almost exclusively in the brain. Cholesterol 24-hydroxylase catalyzes
31 cholesterol into 24S-hydroxycholesterol (24S-OHC), which is a major pathway for excess cholesterol to efflux from the brain. In AD, the levels of 24S-OHC in the brain are decreased, therefore, brain can not excrete excess cholesterol and more cholesterol remains in the brain. This year Famer el al. found that
24S-OHC increases the α-secretase activity as well as the α/β-secretase activity ratio. So when the brain 24S-OHC level is low in AD cases, the cholesterol level in the brain is high, α-secretase activity decreases and β- secretase activity increases comparatively. Famer et al suggested up- regulation of CYP46A1 could be a possible strategy for AD drug development.
Numerous lines of investigations indicate that α-secretase activity can also be regulated by neurotransmitters and their receptors, particularly G- protein coupled receptors (GPCRs). GPCRs are a family of conserved proteins that contain seven transmembrane domains whose functions are diverse but primarily function to transduce extracellular stimuli into the nuclei of the cells and induce transcription of target genes. GPCRs are of such clinical significance that nearly 50% of all drugs target GPCRs. Interestingly but not surprisingly it was recently found that APP processing is also controlled by
GPCRs. Specifically, α-secretase activity can be modulated by GPCRs, like muscarinic acetylcholine receptors (mAChR), metabotropic glutamate receptor
(mGluR) and purinergic receptor P2Y2. The underlying mechanism is still not completely identified but it appears that GPCRs sense the extracellular changes, including cholesterol levels, reactive oxygen species (ROS), hypoxia, and free nucleotide and then transduce these signals to the interior of the cells and induce the transcription of kinases and α -secretase. For example after M1
(a subtype of mAChRs) is activated by its agonist AF267B, protein kinase C
32 (PKC), phospholipase A2 (PLA2) , in turn ADAM17 is activated, stimulating
APP processing preferentially toward non-amyloidogenic pathway. Animal models have also been demonstrated that AF267B can reduce Aβ deposition and rescue the cognitive deficits.
To systematically identify proteins that modulate APP proteolysis, particularly the initial cleavage by α-, or β- secretase, Lichtenthaler and his group carried out an expression cloning screen last year. In addition to protein kinase A, a already known activator, they also identified the following proteins: the endocytic proteins endophilin A1 and A3, the metabotropic glutamate receptor 3 (mGluR3), palmitoyl-protein thioesterase 1 (PPT1), Numb-like and the kinase MEKK2. Endophilin A3 demonstrated the strongest activity of APP shedding, which specifically increased sAPPα secretion and nonspecifically reduced the rate of APP endocytosis. The gene encoding Endophilin A3 is
SH3GL3 (SH3-domain GRB2-like 3), which is located on the chromosome
15q25. This region is near a genomic region (15q26) linked to AD. Endophilin
A3 contains 347 amino acids and is preferentially expressed in the brain and testis. It can selectively interacts with the HDex1p (HD exon 1 protein) and promotes the formation of insoluble polyglutamine-containing aggregates. It is suggested to be involved in the neuronal death and pathophysiology of
Huntington’s disease.
Numerous lines of research have indicated that elevation of non- amyloidogenic pathway is a promising therapeutic strategy for the treatment of
AD. Some potential therapeutics and lead compound exist already. Specifically a benzolactam derivative, TPPB [(2S,5S)-(E,E)-8-(5-(4-(trifluoromethyl)phenyl)-
2,4-pentadienoylamino) benzolactam] is a novel α-secretase activator that
33 functions by stimulating the PKC pathway with unidentified mechanism. It shifts
APP processing towards the non-amyloidogenic pathway by increasing α- secretase activity, as well as decreases β-secretase activity and Aβ40 levels.
An extract from green tea, EGCG, [Green tea polyphenol (-)-epigallocatechin-3- gallate] reduces brain Aβ levels by increasing α-secretase activity, specifically
ADAM10, but does not significantly alter β-or γ-secretase activities. Other examples include acetylcholinesterase inhibitors (AChEIs), 3-hydroxy-3- methylglutaryl-CoA reductase (HMG-CoA) inhibitors (belonging to cholesterol lowing agents, like statins) and nonsteroidal anti-inflammatory drugs (NSAIDs).
Besides their original primary effects, they all can stimulate α-secretase activity, and some may also inhibit β and/or γ activity as well. Donepezil (belonging to
AChEIs) increases α-secretase secretion by promoting ADAM10 trafficking and/or maturation. NSAIDs and statins exert functions by modulation of the isoprenoid pathway and Rho-associated protein kinases (ROCKs), particularly
ROCK1 activities, as well as by modulation of inflammatory reactions and cytokine secretions. It has been well known that caloric restriction (CR) can expand life span and recent findings suggest that CR may be beneficial to AD intervention. by increasing sAPPα. The underlying mechanism involves SIRT1, the NAD+-dependent sirtuin, whose encoding gene lies on chromosome 10, a genetic region linked to AD. CR, as well as resveratrol, an agonist of SIRT1 can dramatically increase sAPPα. This process can be potentially derived from the increase of ADAM10 levels. Resveratrol is an extract from red wine; therefore, selecting food intake should be considered an intervention of AD.
These examples suggest that therapeutically targeting the α-secretase pathway is a viable option fro treating AD. α-secretase have other membrane
34 protein substrates other than APP. More research is needed to investigate the effects apart from APP upon α-secretase stimulation. Detailed understanding of the underlying mechanisms of these α-secretase modulators of APP processing, as well as identifying novel and specific α-secretase modulators will open up novel avenues for the therapeutic intervention of AD.
35
CHAPTER 2 An AICD-based functional screen to identify APP metabolism regulators
Can Zhang1,*, Preeti J. Khandelwal1,*, Ranjita Chakraborty1, Trinna L. Cuellar1, Srikant Sarangi1, Shyam A. Patel1, Christopher P. Cosentino1, Jeremy C. Lee1, Rudolph E. Tanzi2, Aleister J. Saunders1,3, §
1Department of Bioscience & Biotechnology, Drexel University, Philadelphia, PA, USA 2Genetics and Aging Research Unit, MassGeneral Institute for Neurodegenerative Diseases (MIND), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA 3Department of Biochemistry & Molecular Biology, Drexel University College of Medicine, Philadelphia, PA, USA
*These authors contributed equally to this work.
§Corresponding author
Current Email addresses: CZ: [email protected] PJK: [email protected] RC: [email protected] TLC: [email protected] SS: [email protected] SAP: [email protected] CPC: [email protected] JCL: [email protected] RET: [email protected] AJS: [email protected]
36 Abstract Background. A central event in Alzheimer’s disease (AD) is the regulated intramembraneous proteolysis of the β-amyloid precursor protein (APP), to generate the β-amyloid (Aβ) peptide and the APP intracellular domain (AICD).
Aβ is the major component of amyloid plaques and AICD displays transcriptional activation properties. We have taken advantage of AICD transactivation properties to develop a genetic screen to identify regulators of
APP metabolism. This screen relies on an APP-Gal4 fusion protein, which upon normal proteolysis, produces AICD-Gal4. Production of AICD-Gal4 induces
Gal4-UAS driven luciferase expression. Therefore, when regulators of APP metabolism are modulated, luciferase expression is altered.
Results. To validate this experimental approach we modulated α-, β-, and
γ-secretase levels and activities. Changes in AICD-Gal4 levels as measured by
Western blot analysis were correlated to the observed changes in AICD-Gal4 mediated luciferase activity. To determine if a known regulator of APP trafficking/maturation and Presenilin1 endoproteolysis could be detected using the AICD-Gal4 mediated luciferase assay, we knocked-down Ubiquilin 1 and observed decreased luciferase activity. We confirmed that Ubiquilin 1 modulated AICD-Gal4 levels by Western blot analysis and also observed that
Ubiquilin 1 modulated total APP levels, the ratio of mature to immature APP, as well as PS1 endoproteolysis.
Conclusions. Taken together, we have shown that this screen can identify known APP metabolism regulators that control proteolysis, intracellular trafficking, maturation and levels of APP and its proteolytic products. We
37 demonstrate for the first time that Ubiquilin 1 regulates APP metabolism in the
SH-SY5Y, human neuroblastoma, cell line.
38 Background Alzheimer’s disease (AD) is characterized by significant accumulation of cerebral amyloid plaques and intraneuronal neurofibrillary tangles. Amyloid plaques are composed mainly of the β-amyloid peptide (Aβ). Aβ is a normal product of amyloid precursor protein (APP) metabolism. Several genes have been identified encoding enzymes that directly metabolize APP to generate Aβ; however, it is not fully understood how APP metabolism is regulated. Here we describe and validate a novel experimental approach for identifying genes encoding regulators of APP metabolism.
Aβ is generated by the successive proteolytic processing of APP, a process referred to as regulated intramembrane proteolysis (RIP) [50, 99, 100]. RIP occurs when a transmembrane protein is cleaved within the transmembrane domain, releasing a cytoplasmic fragment that can activate gene expression in the nucleus [99]. RIP requires two cleavage events; the first, outside the membrane, often in response to ligand binding, can trigger the second, intramembraneous, cleavage. RIP liberates small, intracellular protein domains that are involved in nuclear signaling processes [99, 100]. Therefore, regulation of RIP is critical for controlling nuclear signaling. Identifying the regulatory mechanisms controlling these proteolytic steps is important for a fuller understanding of these processes.
APP is a type I transmembrane glycoprotein and is suggested to function in neuroprotection, synaptic transmission, signal transduction, and axonal transport [101, 102]. Upon being synthesized, APP undergoes maturation in the protein secretory pathway. APP is N-glycosylated in the ER and cis-Golgi
39 followed by O-glycosylation in medial- and trans-Golgi. RIP of APP can occur via two alternative routes: amyloidogenic and non-amyloidogenic. In amyloidogenic processing, APP undergoes sequential cleavage by β-secretase
(BACE) and γ-secretase to generate Aβ [23]. BACE cleavage occurs in the
APP extracellular domain to produce a soluble extracellular fragment called sAPPβ and a membrane associated, 99-residue C-terminal fragment called
C99 [39] The C99 fragment is a substrate for subsequent cleavage by the γ- secretase complex [51, 103]. The active γ-secretase complex is composed of the amino- and carboxy-terminal fragments of presenilin1 (PS1), a highly glycosylated form of nicastrin (NCSTN), Aph1α and Pen-2 [51, 103]. The amino- and carboxy –terminal fragments of PS1 (~27 and ~17 kDa respectively) are derived by endoproteolytic cleavage of the inactive, full length
PS1 protein within the large hydrophilic loop that spans between transmembrane helices 6 and 7 and are thought to interact with each other
[104]. The products of γ-secretase cleavage are the cytoplasmic APP
Intracellular Domain (AICD) fragment and Aβ peptides of varying length, mainly
40 and 42 residues long [105-107]. In non-amyloidogenic processing, the initial extracellular cleavage of APP is catalyzed by one of a group of proteases termed α-secretases. These enzymes include ADAM9, ADAM10, and ADAM17
(TACE). α-secretase cleavage produces a soluble extracellular fragment called sAPPα and a membrane associated, 83-residue C-terminal fragment called
C83. This C83 fragment is then cleaved by the γ-secretase complex to produce
AICD and a p3 peptide, which is not involved in amyloidogenesis [23].
40 A common feature of RIP processing is the liberation of an intracellular protein domain that initiates nuclear signaling [99, 100]. In the case of APP processing, nuclear signaling can be initiated by the production of the intracellular AICD fragment. Once generated by γ-secretase, the AICD fragment can be stabilized and transported to the nucleus by the cytoplasmic adaptor protein Fe65 [108, 109]. Upon entering the nucleus the AICD/Fe65 complex can form a tripartite, transcriptionally active complex with the histone acetyltransferase Tip60 [66, 110]. Consistent with this model, cells concomitantly over-expressing an APP-Gal4-DNA binding domain fusion protein and Fe65, and carrying a Gal4 UAS-driven reporter construct display a
>2000 fold increase in reporter transcription compared to cells over-expressing just the Gal4 DNA binding domain and Fe65 [66]. This increase in transactivation activity is dependent on Tip60 and can be abolished when the interaction between AICD and Fe65 is disrupted by mutagenesis of the AICD
NPTY motif, the binding site for Fe65 [66]. However, these data do not rule out a possible effect of full-length APP in inducing nuclear signaling. Indeed, APP nuclear signaling can occur in the absence of γ-secretase activity and therefore does not require the AICD fragment [111]. The relative contribution of AICD- mediated versus holo-APP mediated nuclear signaling is not clear at this time
[66, 110, 111].
The genomic targets of AICD- or APP-mediated nuclear signaling are not clearly defined. APP, BACE, Tip60, GSK-3β, Mn-SOD, KAI1, NEP and other genes have all been reported to be targets of APP mediated transcriptional activation [112-115]; however, there is a paucity of confirmatory reports [111].
41 At this time, the biological role of AICD-mediated transactivation is not clear
[113, 116, 117]. Despite this confusion, evidence suggests that defective APP signaling is involved in AD pathogenesis [118-122].
Given the centrality of APP in AD, it is crucial to identify regulators of APP metabolism, including, but not limited to, APP proteolysis. Regulation of APP metabolism can occur by numerous mechanisms, including regulation of APP transcription, APP translation, APP maturation, intracellular trafficking of full- length APP and APP cleavage products, APP proteolysis, and APP degradation. While Komano and colleagues have used a genetic screen to specifically identify regulators of γ-secretase activity [123], a screen that will identify APP metabolism regulators that act through multiple mechanisms is needed.
Here we describe a novel experimental approach to identify a variety of regulators of APP metabolism. We use an AICD-Gal4 mediated luciferase expression assay as a general reporter of APP metabolism in the human neuroblastoma cell lines, SH-SY5Y. To validate this assay, we utilized pharmacologic agents, as well as forward and reverse genetics, to modulate
APP proteolysis, AICD trafficking and AICD transactivation. To determine if regulators of APP maturation and PS1 endoproteolysis also can be detected with this screening approach, we knocked-down Ubiquilin 1 and observed decreased AICD-Gal4 luciferase activity. Using Western blot analysis, we show that Ubiquilin 1 controls APP levels, the ratio of mature to immature APP, as well as presenilin1 endoproteolysis, confirming the previously reported role of
Ubiquilin 1 in APP and presenilin1 metabolism in non-neuronal human cell lines
[124-127]. Taken together, our results validate the use of the AICD-Gal4
42 mediated luciferase assay in combination with forward and reverse genetics as a screen to identify APP metabolism regulators.
43 Results
Establishment of a functional assay to identify APP metabolism regulators. We utilized the APP-Gal4 / Gal4-UAS luciferase reporter system
(Figure 1A) developed by Cao and Südhof [66]. We established this assay system in our laboratory by creating a SH-SY5Y, human neuroblastoma, cell line that stably expresses the assay components. Three different stable cell lines have been generated; all stably carry a luciferase reporter gene under the control of the Gal4-UAS (Gal4-UAS luciferase). In addition to this reporter gene, one cell line expresses the Gal4 DNA binding domain alone (SY5Y-Gal4), the second cell line expresses APP695 fused to the Gal4 DNA binding domain
(SY5Y-APP-Gal4), and the third cell line expresses a mutated version of
APP695-Gal4 (SY5Y-APP*-Gal4). This mutation in APP alters the NPTY motif
(P685A; Y687A) and disrupts Fe65 binding to this site [66]. Once these cells were established, luciferase assays were performed to determine the relative luciferase activity of the cell lines (Figure 1B). SY5Y-APP-Gal4 cells have a statistically significant (p < 0.01) ~20 fold increase in luciferase activity compared to SY5Y cells expressing either Gal4 or APP*-Gal4.
Pharmacologic modulation of α- and γ-secretase activity alters AICD-
Gal4 mediated luciferase activity. To determine if monitoring AICD-Gal4 mediated luciferase activity is a valid method to detect alterations in APP metabolism, we used pharmacologic agents known to modulate APP proteolytic processing and compared the effects of these agents on levels of APP proteolytic products and AICD-Gal4 mediated luciferase activity. To accomplish this, we treated our SY5Y-APP-Gal4 cells with pharmacologic
44 modulators of α-, β-, and γ-secretase activity and measured the effects using
Western blot analyses for APP cleavage products as well as AICD-Gal4 mediated luciferase activity.
L-685,458 is a transition state inhibitor of γ-secretase that prevents Aβ and AICD generation [128]. We treated SY5Y-APP -Gal4 cells with L-685,458
(2.5 μM for 10 hours) or with vehicle (DMSO) and collected cell lysates. We performed Western blot analysis on these cell lysates using an antibody to the
C-terminus of human APP. In vehicle treated cells we observe bands migrating at ~28 and ~26 kDa (Figure 2A). Cao and Sudhof observed a similar doublet at approximately the same relative molecular weight. They identified these bands as C83-Gal4 and AICD-Gal4, respectively. In L-685,458 treated cells the intensity of the C83-Gal4 band is significantly increased seven-fold (p<0.01) and the AICD-Gal4 band is significantly decreased by 80% (p<0.01;Figure 2B).
These results are consistent with the substrate / product relationship between
C83-Gal4 and AICD-Gal4. The size difference between C83-Gal4 and AICD-
Gal4 is what is expected for γ-secretase cleavage of C83-Gal4. It is also interesting to note that normally AICD is difficult to detect by Western blot analysis, however the AICD-Gal4 fusion levels are quite high. This suggests that the AICD-Gal4 fusion must protect AICD-Gal4 from degradation by IDE and/or other proteases [129].
L-685,458 treatment of SY5Y-APP-Gal4 cells results in a concentration- dependent decrease in AICD-Gal4 mediated luciferase activity; at a concentration of 2.5 μM there is a ~75% decrease in luciferase activity (Figure
45 2). The L-685,458 concentration required for 50% inhibition of AICD-Gal4 mediated luciferase activity is 1.25 μM.
The phorbol ester, PMA (phorbol 12-myristate 13-acetate), stimulates α- secretase activity [25]. Treatment of SY5Y-APP-Gal4 cells with PMA (1 μM for
10 hours) increased levels of the α-secretase cleavage products sAPPα and
C83-Gal4, four-fold and two-fold, respectively (p < 0.01; Figures 2D & 2E). In addition, Western blot analysis revealed a two-fold increase in AICD-Gal4 levels (p < 0.01; Figure 2D & 2E). This two-fold increase in AICD-Gal4 levels suggested that a similar PMA-induced increase in AICD-Gal4 mediated luciferase activity should be observed. Indeed, when we measured luciferase activity as a function of increasing PMA concentration (Figure 2F), we observed a dose-dependent increase in luciferase activity. The PMA-induced increases in luciferase activity plateaus at 50 nM PMA. At concentrations of 50 nM and higher, we observed approximately a two-fold increase in AICD-Gal4 mediated luciferase activity in close agreement with the observed two-fold increase in
AICD-Gal4 by Western blot analysis.
TAPI-1 (Tumor necrosis factor-α protease inhibitor 1) inhibits α-secretase mediated shedding of the APP ectodomain [37]. Treating the SY5Y-APP-Gal4 cells with TAPI-1 (20 μM for two hours; Figure 2G & 2H) resulted in a modest, yet significant decrease of sAPPα (31%, p<0.05), C83-Gal4 (27%, p<0.01) and
AICD-Gal4 levels (36%; p<0.01), as well as in AICD-Gal4 mediated luciferase activity (38%; p<0.01). In addition, TAPI-1 exhibits a dose-dependent effect with 20 μM resulting in a 37% decrease in AICD-Gal4 mediated luciferase activity (Figure 2I). Again, these data show that alterations in AICD-Gal4 levels
46 as detected by Western blot can be accurately detected by the AICD-Gal4 mediated luciferase assay.
Finally, we treated SY5Y-APP-Gal4 cells with a β-secretase inhibitor (β- secretase inhibitor II). This inhibitor prevents BACE-mediated cleavage of APP and generation of Aβ [130]. Treating these cells resulted in no observable change in AICD-mediated luciferase activity. This result is not surprising given the very low levels of β-secretase cleaved APP (C99-Gal4) that we observe in these cells compared to the high levels of α-secretase cleaved APP (C83-Gal4) we observe (Figures 2A, 2D, 2G). We estimate that of all the APP molecules undergoing α- or β-secretase cleavage only about 10% are cleaved by β- secretase, using our Western blot data (data not shown). Therefore, inhibition of BACE, even if effective, may result in an undetectable change in the levels of cleavage products in this experimental scheme.
In summary, pharmacologic modulation of α- and γ-secretase activities alters AICD-Gal4 mediated luciferase activities that accurately correspond to the changes in AICD-Gal4 levels determined by Western blot analysis.
Genetic manipulation of secretase levels modulates AICD-Gal4 mediated luciferase activity. To further validate the AICD-Gal4 mediated luciferase assay as a reporter of APP metabolism, we over-expressed and knocked-down the expression of genes involved in α-, β- & γ-secretase activities. Again, we compared the effects of over-expression or knock-down on levels of APP proteolytic products quantified by Western blot analysis to AICD-
Gal4 mediated luciferase activity in SY5Y-APP-Gal4 cells.
47 Over-expression experiments were conducted by transiently transfecting individual over-expression plasmids or empty vector controls into the SY5Y-
APP-Gal4 cells. Cell lysates and conditioned media were collected 24 – 48 hours post transfection. ADAM10 and ADAM17 over-expression promoted α- secretase cleavage of APP and increased sAPPα secretion as compared to
"empty vector" transfected cells (Figure 3A – 3D). ADAM10 and ADAM17 over- expression also significantly increased C83-Gal4 and AICD-Gal4 levels as detected by Western blot (Figure 3A – 3D). Specifically, AICD-Gal4 levels increased approximately three-fold for both (Figure 3B & 3D). Measuring AICD-
Gal4 mediated luciferase activity, we found that over-expression of ADAM10 and ADAM17 resulted in a statistically significant three to four fold increase in luciferase activity (Figure 3E). Furthermore, over-expression of the β-secretase gene (BACE) or individual components of the γ-secretase complex (PSEN1,
PEN2, APH1, and NCSTN) or another α-secretase member ADAM9 also results in increased luciferase activity (Figure 3E). Specifically, BACE over- expression significantly increased luciferase activity approximately two fold (p <
0.01), PEN2 and NCSTN over-expression increased luciferase activity up to two fold (p < 0.01.) However, over-expression of PSEN1 and APH1 did not result in any significant change in luciferase activity.
We knocked-down the genes responsible for α- and γ-secretase using commercially available shRNAs [96]. A control shRNA, which is not complementary to any known human gene, was used as a negative control.
SY5Y-APP-Gal4 cells were transfected with individual shRNAs and selected with 2 μg/ml puromycin for 5 to 7 days. Conditioned media and cell lysates
48 were collected from these cells and utilized for Western blot analyses and luciferase assays. shRNAs specific for APP, ADAM10, and ADAM17 were tested for their ability to knock-down their target genes (Figure 4A, 4B, 4C).
Knock-down of these target genes was robust and we have observed significant protein knock-down with at least two different shRNA sequences for each target gene. Consistent with this knock-down of ADAM 10 and ADAM17, sAPPα levels were decreased significantly (Figure 4D & 4E). In addition,
Western blot analyses showed AICD-Gal4 levels were also decreased when
APP, ADAM10 and ADAM17 were knocked-down (Figure 4A, 4B, 4C). Knock- down of these target genes also decreased AICD-Gal4 mediated luciferase activity (Figure 4F). Specifically, APP knock-down significantly decreased luciferase activity about 80% (p < 0.01); furthermore α-secretase (ADAM10, and ADAM17) knock-down significantly decreased luciferase activity 40-60% (p
< 0.01). Individual γ-secretase components, PSEN1, Pen2, APH1, and NCSTN, were also knocked-down and this resulted in significant 30-50% decreases in luciferase activity (p < 0.01).
Genetic manipulation of Fe65 and Tip60 levels modulates AICD-Gal4 mediated luciferase activity. To determine if changes in AICD metabolism modulated AICD-Gal4 mediated luciferase activity, we over-expressed and knocked-down Fe65 and Tip60. Transient over-expression of Fe65 significantly increased luciferase activity more than two-fold (p < 0.01), while transient over- expression of Tip60 resulted in a 30% increase in luciferase activity that was not significant (Figure 5A). Knock-down of Fe65 and Tip60 resulted in a significant 40-50% decrease in luciferase activity (Figure 5B; p < 0.01).
49 Ubiquilin 1 regulates AICD-Gal4 levels. Having shown that monitoring
AICD-Gal4 mediated luciferase activity accurately measures changes in AICD-
Gal4 levels induced by changes in secretase activity/levels, we wanted to determine if this approach could detect regulators with a less direct role in APP proteolysis and AICD signaling. We decided to test Ubiquilin 1 because (i) the gene encoding Ubiquilin 1 (UBQLN1) is located in a region of chromosome 9 that displays linkage to AD in several independent samples [131-136], (ii) a polymorphism in UBQLN1 modulates AD risk in several independent samples
[137-139], (iii) Ubiquilin 1 can modulate APP trafficking to the cell surface in
HEK-293 and H4 cell lines [124], and (iv) Ubiquilin 1 can modulate γ-secretase activity, though the consequences of this modulation on γ-secretase substrates were not determined [125-127]. Given this, testing Ubiquilin 1 would determine if our genetic screen can detect regulators of APP trafficking and presenilin endoproteolysis. Furthermore, the role of Ubiquilin 1 in APP metabolism regulation has not been previously investigated in SH-SY5Y cells.
SY5Y-APP-Gal4 cells were transfected separately with five different
Ubiquilin 1 shRNAs, APP shRNA and the control shRNA. Cell lysates were collected and utilized for luciferase assays. Individually, all five Ubiquilin 1 shRNA constructs significantly decreased luciferase activity. They resulted in
50% (p < 0.01), 60% (p < 0.01), 40% (p < 0.01), 60% (p < 0.01), and 60% (p <
0.01) decreases in luciferase activity, respectively (Figure 6A) as compared to cells expressing the control shRNA. To confirm the role of Ubiquilin 1 in AICD- mediated transcriptional activity suggested by these results, we transiently over-expressed Ubiquilin 1 in SY5Y-APP-Gal4 cells and measured luciferase activity. We observed that Ubiquilin 1 over-expression resulted in an
50 approximately 90% (p < 0.05) increase in luciferase activity compared to the empty vector control (Figure 7A).
Ubiquilin 1 regulates APP and PS1. To begin to gain insight into the mechanism(s) by which Ubiquilin 1 modulates AICD-Gal4 mediated luciferase expression, we utilized Western blot analysis of cell lysates and conditioned media from SY5Y-APP-Gal4 cells in which Ubiquilin 1 was knocked-down or over-expressed to monitor APP and Ubiquilin 1 metabolism. Specifically, we analyzed cell lysates and conditioned media of cells expressing Ubiquilin 1 shRNA number 2, since transfection with this shRNA led to the largest decrease in luciferase activity. Expression of this shRNA resulted in a robust
Ubiquilin 1 knock-down and led to significantly decreased levels of mature full- length APP, immature full-length APP, AICD-Gal4, C83-Gal4, and sAPPα
(Figures 6B & 8). To determine if Ubiquilin 1-induced changes in APP mRNA levels underlie the observed changes in full length APP levels, we performed real-time, quantitative PCR on SY5Y-APP-Gal4 cells stably expressing either control or Ubiquilin 1 shRNAs. No Ubiquilin 1-induced changes in APP mRNA levels were observed (Figures 6C). This suggests that Ubiquilin 1 regulation of full-length APP levels occurs post-transcriptionally.
We studied Ubiquilin 1 over-expression to determine if we observed the converse effects on APP proteolytic products and full-length APP. Indeed, we observed that Ubiquilin 1 over-expression resulted in increased levels of mature and immature full-length APP, AICD-Gal4, C83-Gal4, and sAPPα
(Figures 7B & 8).
The Ubiquilin 1-induced effects on full-length APP are greater on mature
APP levels than on immature APP levels (Figure 8). This results in a decrease
51 in the ratio of mature to immature full-length APP-Gal4 when Ubiquilin 1 is knocked-down (p < 0.01; Figure 8) and an increase in this ratio when Ubiquilin
1 is over-expressed (p < 0.05; Figure 8).
Since Ubiquilin 1 has been reported to regulate PS1 endoproteolysis in
HEK-293 cell lines, we sought to determine if the Ubiquilin 1-induced changes that we observed in AICD-Gal4 and C83-Gal4 levels may be due in part to changes in PS1 endoproteolysis [127]. Ubiquilin 1 knock-down in SY5Y-APP-
Gal4 cells decreased PS1 carboxy-terminal fragment levels (PS1-CTF; Figure
6D) and Ubiquilin 1 over-expression increased PS1-CTF levels (Figure 7C). We did not observe any changes in the levels of ADAM 10, ADAM 17 or BACE when Ubiquilin 1 was over-expressed or knocked-down (data not shown).
Finally, we over-expressed Ubiquilin 1 in naïve SH-SY5Y to ensure that the results we observed are not limited to the SY5Y-APP-Gal4 cell line. We found that in these naïve cells Ubiquilin 1 over-expression resulted in increased total, mature and immature APP, sAPP as well as PS1 CTF, consistent with our findings in SY5Y-APP-Gal4 cells (data not shown).
52 Discussion
Taking advantage of the APP intracellular domain’s (AICD) ability to activate transcription, we established an assay to monitor APP metabolism in the human neuroblastoma cell line, SH-SY5Y. We are using this assay in combination with RNAi-mediated knock-down of positional candidate genes as a genetic screen to identify regulators of APP metabolism. Here we describe validation of this experimental approach using pharmacologic and genetic modulation of known APP metabolism regulators. We find that AICD-Gal4 mediated luciferase activity is significantly and accurately changed when secretases, Fe65, Tip60, or Ubiquilin 1 levels / activities are modulated pharmacologically or genetically. The ability of Ubiquilin 1 to regulate APP metabolism in SH-SY5Y cells had not been investigated previously. Our initial findings show that in these cells Ubiquilin 1 regulates total APP levels, APP maturation and PS1 endoproteolysis. Our results lead us to conclude that the genetic screen we describe is capable of identifying genes that encode regulators of APP proteolysis, APP maturation, APP levels, and AICD activity.
Validation of AICD-Gal4 luciferase assay. The functional assay for identifying APP metabolism regulators relies on the ability of an AICD-Gal4 fusion to transactivate a firefly luciferase reporter gene [66]. While the biological role of AICD-mediated transactivation is unclear [113, 116, 117, 140], we utilized this transactivation function purely as a reporter of APP processing and therefore APP metabolism. We determined that monitoring AICD-Gal4 mediated luciferase activity is correlated to AICD-Gal4 levels by utilizing pharmacologic and genetic agents to regulate secretase activities and thereby
53 modulate AICD-Gal4 levels. In SH-SY5Y cells stably expressing an APP-Gal4 fusion protein and a Gal4-UAS driven luciferase reporter construct (SY5Y-APP-
Gal4 cells), we utilized TAPI-1 and L-685,458 to inhibit α- and γ-secretases respectively. TAPI-1 inhibits α-secretase cleavage of APP as well as several other cell surface proteins including TNFα [37]. L-685,458 is a potent and selective cell-permeable γ-secretase inhibitor [63]. Both of these inhibitors decreased AICD-Gal4 levels and decreased AICD-Gal4 mediated luciferase activity to similar levels. Inhibiting BACE activity did not have an appreciable effect on AICD-Gal4 levels or AICD-Gal4 mediated luciferase activity, which is not surprising since the majority of APP processing occurs via the α-secretase pathway in SH-SY5Y cells. Stimulation of α-secretase using PMA [141] increased AICD-Gal4 levels and increased AICD-Gal4 mediated luciferase activity to similar levels.
To further validate our functional assay, we over-expressed and knocked- down genes that encode the α-, β-, and γ-secretases. Similar to the effects of pharmacologic modulators of secretases, over-expressing or knocking-down secretase genes resulted in predictable alterations in AICD-Gal4 levels as measured by Western blot analysis. The changes in luciferase activity induced by secretase over-expression or knock-down mirrored the trends observed in the Western blot analysis. Knock-down of APP had the most dramatic effect on
AICD-Gal4 mediated luciferase activity while knock-down of genes encoding α- and γ-secretases resulted in significant decreases in AICD-Gal4 mediated luciferase.
54 To assess the quality of the AICD-Gal4 mediated luciferase assay we calculated the “Z-factor” for the assay in response to the known APP metabolism modulators[142]. The Z-factor is a dimensionless metric that takes assay dynamic range and data variation into consideration to assess the utility and reliability of the assay. Scores between 0.5 and 1.0 indicate an excellent assay [142]. Using the data we collected, we calculated Z-factors for pharmacologic and genetic modulation of the secretases (Table 2-1). For all of these conditions we obtain Z values between 0.5 and 1.0, indicating that our experimental approach is robust and has the capability of identifying APP metabolism regulators that increase or decrease AICD generation.
AICD metabolism regulators modulate AICD-Gal4 luciferase activity. AICD-
Gal4 mediated transactivation has been shown to require Fe65 and Tip60.
Fe65 is an adaptor protein that binds to the NPTY sequence in AICD and mediates intracellular trafficking of AICD-Gal4 from the cytoplasm into the nucleus [66]. Once inside the nucleus, the AICD-Gal4/Fe65 complex recruits the histone acetyltransferase, Tip60. Fe65 and Tip60 are both required for
AICD-Gal4 transactivation activity. We observed increased AICD-Gal4 mediated luciferase activity when we over-expressed Fe65 or Tip60 and decreased luciferase activity when either of these genes was knocked-down.
Ubiquilin 1 modulates APP metabolism in SH-SY5Y cells. Having validated our experimental approach using direct regulators of APP proteolysis and
AICD-metabolism, we then sought to determine if Ubiquilin 1 could modulate
AICD-Gal4 mediated luciferase activity. Ubiquilin 1 has been shown to regulate presenilin1 endoproteolysis and APP trafficking in HEK-293 cells [124-127] and therefore testing Ubiquilin 1 would help to determine whether our experimental
55 approach could detect APP metabolism regulators that are not directly involved in APP proteolysis nor in AICD signaling. When Ubiquilin 1 was knocked-down,
AICD-Gal4 luciferase activity was significantly decreased.
Ubiquilin 1 is a conserved protein that contains an NH2-terminal ubiquitin- like domain (UBL) and a COOH-terminal ubiquitin-associated (UBA) domain
[125]. Through these domains, Ubiquilin 1 associates with ubiquitin ligases and the proteosome and is proposed to link ubiquitination with proteosome- mediated protein degradation. This suggests that Ubiquilin 1 plays a role in responding to protein misfolding, aggregation, and/or stress [125, 143]. In the brains of AD patients, there is increased Ubiquilin 1 in neurons containing neurofibrillary tangles (NFTs), as compared to control brains [125]. In the brains of Parkinson’s disease patients, as well as patients with diffuse Lewy body disease (DLBD), there is strong Ubiquilin 1 staining of Lewy bodies [125].
Finally, a polymorphism in the UBQLN1 gene has been shown to increase AD risk in family-based and large case-control samples [137-139].
The role of Ubiquilin 1 in AD pathogenesis may be due to its ability to regulate formation of active γ-secretase complexes and/or regulate APP trafficking [124-127]. Monteiro and colleagues have found that Ubiquilin 1 can regulate full-length Presenilin1 (PS1), Presenilin2 (PS2), Nicastrin, and PEN-2 levels as well as PS1 and PS2 endoproteolysis [125-127]. Specifically, Ubiquilin
1 over-expression increased full-length presenilin (PS1 and PS2) levels in HeLa cells. In HEK-293 cells, Ubiquilin 1 over-expression decreased presenilin endoproteolysis while Ubiquilin 1 knock-down increased presenilin endoproteolysis [127]. In addition, Monteiro and colleagues show that nicastrin and Pen-2 levels are decreased by Ubiquilin 1 over-expression and increased
56 by Ubiquilin 1 knock-down in HEK-293. In addition to these effects on γ- secretase components, Hiltunen and colleagues reported that Ubiquilin 1 knock-down decreased steady-state full-length immature APP levels, increased trafficking of APP from intracellular compartments to the cell surface, and increased steady-state sAPPα levels in HEK-293 and H4 cell lines [124]. These effects on APP levels and secretion altered Aβ40 and Aβ42 levels. However,
Ubiquilin 1 knock-down did not alter α-, β-, or γ-secretase levels or C83 and
C99 levels in these cell lines.
Here we found that in the human neuroblastoma cell line, SH-SY5Y,
Ubiquilin 1 regulates total full-length APP, the ratio of mature to immature APP, as well as PS1 endoproteolysis. To arrive at these conclusions, we over- expressed and knocked-down Ubiquilin 1 in SY5Y-APP-Gal4 cells and monitored APP metabolism using Western blot analysis. We found that
Ubiquilin 1 knock-down decreased levels of AICD-Gal4, C83-Gal4, sAPPα, full- length mature and immature APP, and the ratio of mature to immature APP.
Ubiquilin 1 over-expression elicited the opposite effect on the levels of these molecules.
The fact the ratio of mature to immature APP is altered by Ubiquilin 1 in the absence of APP mRNA level changes suggests that Ubiquilin 1 modulates trafficking through the secretory pathway in SH-SY5Y cells. This conclusion was reach by Hiltunen and colleagues when investigating the role of Ubiquilin 1 on APP metabolism in H4 and HEK-293 cell lines [124].
Given the existing reports that Ubiquilin 1 regulates PS1 levels and endoproteolysis in HeLa and HEK-293 cells, respectively, we sought to
57 determine if the observed changes in APP processing may be due, in part, to
Ubiquilin 1 mediated changes in PS1 metabolism [125-127]. Interestingly,
Hiltunen and colleagues did not observe any changes in PS1 levels or endoproteolysis upon transient Ubiquilin 1 knock-down in HEK-293 [124]. In
SY5Y-APP-Gal4 cells, we observed that Ubiquilin 1 knock-down decreases
PS1 endoproteolysis and Ubiquilin 1 over-expression promotes PS1 endoproteolysis. Presumably these changes in PS1-CTF levels alter γ- secretase activity and cleavage of other γ-secretase substrates. At this time, it is not clear how Ubiquilin 1 regulates PS1 endoproteolysis. No alterations in
ADAM10, ADAM17, or BACE levels were observed when Ubiquilin 1 was knocked-down or over-expressed. These results suggest that Ubiquilin 1 regulates APP metabolism not only by controlling the ratio of mature to immature APP but also by post-transcriptionally controlling total APP (mature and immature) levels and PS1 endoproteolysis.
It is interesting to note that the effects of Ubiquilin 1 over-expression / knock-down on APP and presenilin metabolism that we observe in SH-SY5Y cells are different than those observed in HEK-293 and HeLa cells [124, 127].
In SH-SY5Y cells we find Ubiquilin 1 knock-down decreased total, mature, and immature full-length APP, sAPPα, C83 and AICD steady-state levels and the ratio of mature to immature APP, while over-expression increased these same steady-state levels. In addition, Ubiquilin 1 over-expression increased PS1 endoproteolysis. In HEK-293 cells, Hiltunen et al. found that Ubiquilin 1 knock- down decreased steady-state immature full-length APP levels, increased sAPPα levels, and no effects were observed in C83, C99, AICD, and PS1 CTF
58 levels [124]. However, Massey et al. observed an increase in PS1 endoproteolysis in HEK-293 cells [127]. In SH-SY5Y cells however, Ubiquilin 1 seemingly has opposite effects on APP and presenilin metabolism than observed in HEK-293. At this time the reasons for these differences are not clear; they could be due to differences in experimental procedure (e.g. differences in cell confluency, and/or RNAi techniques [transient siRNA versus stable shRNA]) and/or inherent differences in these two cell types. One of the noticeable differences between these cells is that in SH-SY5Y cells, the majority of full-length APP is mature, whereas in HEK-293 the majority of full- length APP is immature (data not shown). Cell type dependent effects of
Ubiquilin 1 have been observed previously. In COS7 cells, Ubiquilin 1 over- expression reduced cell surface expression of nicotinic acetylcholine receptors
(nAChRs), while in superior cervical ganglion neurons Ubiquilin 1 over- expression had no effect on nAChR levels [144]. These cell type-dependent effects are interesting given the differential vulnerability observed in AD brains, where subsets of neocortical and hippocampal neurons preferentially degenerate [145]. In addition to these cell type-dependent effects, Ubiquilin 1 has been shown to function in seemingly opposite ways. Ubiquilin 1 over- expression has been shown to promote accumulation of some proteins [HASH-
1[146], HES-1[146], and GABAA receptor[147]] as well as to promote degradation of other proteins [nAChRs[144] and Hepatitis C virus RNA- dependent RNA polymerase [NS5B][148]]. It will be important to study the role of Ubiquilin 1 on APP metabolism in primary neurons and in vivo to determine its true role in regulating APP metabolism and in AD pathogenesis.
59 Our Ubiquilin 1 results suggest that in SH-SY5Y cells, Ubiquilin 1 regulates
APP metabolism not only by controlling the ratio of mature to immature APP but also by post-transcriptionally controlling total APP (mature and immature) levels and PS1-CTF levels.
AICD-Gal4 luciferase assay accurately reports AICD-Gal4 levels. Finally, we were struck by the ability of the AICD-Gal4 mediated luciferase assay to accurately report AICD-Gal4 levels. To determine the correlation between these two, we plotted the change in luciferase activity versus the change in
AICD-Gal4 levels as measured by Western blot analysis. AICD-Gal4 levels were modulated by pharmacologic or genetic modulation of secretases and
Ubiquilin 1. This analysis revealed a strong correlation between AICD levels and AICD-mediated luciferase expression (Figure 9; R = 0.90). In addition, the best fit line of this relationship has a slope close to 1 (m ≅ 1.3) demonstrating that this luciferase assay provides an accurate reporter of changes in AICD-
Gal4 levels. In addition, measuring AICD-Gal4 mediated luciferase activity provides a simple, quick and inexpensive means for monitoring changes in APP metabolism. While this approach can be successfully utilized to identify genes that putatively modulate AICD-Gal4 levels. Additional assays, including as
Western blot and ELISA, will be necessary to confirm their role in APP metabolism regulation and gain insight into the mechanism of regulation.
60 Conclusions
We have established and validated an AICD-Gal4 based functional assay in SH-SY5Y cells. Using this assay in combination with RNAi, we have developed a genetic screen to identify regulators of APP metabolism. This screen accurately, robustly, and easily measures changes in AICD-Gal4 levels.
We demonstrate that these AICD-Gal4 levels can be altered by pharmacologic or genetic modulation of genes that directly regulate APP levels, AICD trafficking/signaling, APP maturation, and APP proteolysis. Using this approach, we show that Ubiquilin 1 can regulate AICD-Gal4 levels in SH-SY5Y cells.
Ubiquilin 1 regulates AICD-Gal4 levels by modulating APP levels, the ratio of mature to immature APP, and PS1 endoproteolysis. Taken together, our results demonstrate that this genetic screen is capable of identifying APP metabolism regulators that can modulate the APP proteolytic processing, APP maturation,
APP levels, and AICD trafficking/signaling.
61 Methods
Chemicals and antibodies: Phorbol 12-myristate 13-acetate (PMA), L-
685,458, and puromycin were purchased from Sigma. TAPI-1 was purchased from Peptides International. β-secretase inhibitor II, N-Benzyloxycarbonyl -Val-
Leu-leucinal Z-VLL-CHO, was purchased from Calbiochem. The APP C- terminal antibody (A8717; 1:1000) and β-actin antibody (1:10,000) were purchased from Sigma. The 6E10, anti-APP antibody was purchased from
Covance and utilized for detection of sAPPα (1:1000). The BACE1 antibody
(1:1000) was purchased from Bioscience. The ADAM10 (C-terminal) antibody
(1:1000) was purchased from ProSci. The Ubiquilin 1 antibody (1:160) was purchased from Zymed. The ADAM17 antibody (1:1000) was purchased from
Chemicon. The HRP-conjugated secondary antibodies (anti-mouse and anti- rabbit) (1:10,000) were purchased from GE.
Plasmids. The plasmids APP-Gal4, APP*-Gal4 and Gal4, Gal4-UAS-luciferase
(encoding firefly luciferase) were kindly provided from Dr. Thomas Südof, and are described elsewhere [66]. Briefly, each of these plasmids encode only the
DNA binding domain of Gal4. The ADAM10 over-expression plasmid was kindly provided by Dr. Paul Saftig. The ADAM9 and ADAM17 were provided by Dr.
Carl Blobel. The empty vector of ADAM9, ADAM10 and ADAM17 is pcDNA3.1.
Ubiquilin 1 over-expression plasmid, which was constructed from pCMV vector, was kindly provided by Dr. Mervyn J. Monteiro.
Cells and cell culture. SH-SY5Y and naïve human embryonic kidney (HEK)-
293 cells were purchased from ATCC. These cell lines were cultured in
Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal
62 bovine serum, 2 mM L-glutamine, 100 units/ml penicillin, and 100 μg/ml streptomycin. SH-SY5Y cells that stably express APP-Gal4, APP*-Gal4, or Gal-
4, and carrying the Gal4-UAS-luciferase reporter construct were constructed by co-transfecting one of the Gal4 constructs, the pCDNA3.1 plasmids, along with the Gal4-UAS plasmid, and selecting resistant clones with 400 μg/ml G418.
These cells were then tested for γ-secretase dependent luciferase activity.
Clonal lines that stably express luciferase were obtained and were maintained with media containing 200 μl/ml G418.
RNAi. Plasmid-based shRNA constructs were purchased from Open-
Biosystems (http://www.openbiosystems.com/). These constructs are part of the human retroviral shRNA library housed at the Drexel University RNAi
Resource Center. We utilized the following target specific shRNAs: for UBQLN1 shRNAs (Open Biosystems catalog #s: v2HS_58534, v2HS_254856, v2HS_254715, v2HS_255129 and v2HS_58531); for ADAM9 shRNAs
(V2HS_17130, V2HS_17127, V2HS_17126, and V2HS_17129); for ADAM10 shRNAs (v2HS_94294, v2HS_94297, and v2HS_94295); for ADAM17 shRNAs
(RHS3979-9619367, RHS3979-9619368, RHS3979-9619369, and RHS3979-
9619370); for BACE1 shRNAs (V2HS_25207, V2HS_25209, V2HS_25206,
V2HS_25205, V2HS_25210); for PSEN1 shRNAs (v2HS_89932, v2HS_89931); for PSEN2 shRNAs (v2HS_93093); for APH1 shRNAs (v2HS_117094, v2HS_117096); for NCSTN shRNAs (v2HS_255892).
As a negative control shRNA, we utilized the non-silencing shRNA from Open
Biosystems, Inc. (RHS 1707). shRNA constructs were transfected using Arrest-
In transfection reagent (Open Biosystems, Inc.) using the conditions suggested
63 by the manufacturer. Stably expressing shRNA clones were generated by adding 2 μg/ml puromycin 24 hours post-transfection. Populations of resistant clones were detected five to seven days post-transfection.
Western Blot Analysis. Cells were lysed in RIPA cell lysis buffer (50 mM Tris-
HCL pH 7.4, 150 mM NaCl, 1 mM EDTA, 1% NP-40, 1mM PMSF and 1 μg/ml aprotinin, 1 μg/ml leupeptin, and 1 μg/ml pepstatin), and centrifuged at 14,000 rpm for 15 minutes at 4○C. The resulting supernatant was transferred to a new micro-centrifuge tube. The protein concentration of the cell lysates was determined using the BCA protein assay kit (Pierce, Rockford, IL) according to the manufacturer’s instructions. Equal quantities of protein were loaded into the wells of 4-12% Bis-Tris polyacrylamide gels (Invitrogen) along with See Blue plus 2 protein marker (Invitrogen). Gels were run using MES running buffer and transferred to PVDF membrane (Immobilon PSQ, Millipore) using a semi-dry transfer apparatus (Owl Scientific) and NuPage transfer buffer (Invitrogen).
PVDF membranes were blocked in TBST with 5% dry milk for at least two hours, washed extensively, then incubated with primary antibody for either one hour at room temperature or overnight at 4○C. After removing the primary antibody, membranes were extensively washed and incubated with either goat- anti-rabbit-HRP or goat-anti-mouse-HRP secondary antibodies (1:10,000; GE) for one hour at room temperature. Membranes were washed and developed using West Dura Extended Duration Substrate (Pierce). The blot was visualized using a FluoroChem 8900 imaging system (Alpha Innotech), and signals were quantified using AlphaEase Fc software. To account for any differences in loading, target band densitometries were divided by actin densitometries
64 obtained from the same lane. These corrected densitometries were normalized to controls in each experiment.
Detection of sAPPα followed the protocol detailed in Lanni et al. and
Bergamaschi et al. [141, 149]. Briefly, conditioned media was collected and
48% trichloroacetic acid (TCA) was added so that the TCA final concentration was 15%. This mixture was incubated on ice for 30 minutes, and centrifuged at
14,000 rpm for 20 minutes. Following this spin, the supernatant was aspirated and discarded. 500 μl of ice cold acetone was used to resuspend the pellet.
This mixture was placed at -20○C for at least 30 minutes, followed by centrifugation at 14,000 rpm for 20 minutes. The supernatant was carefully aspirated and discarded. The remaining pellet was air dried for 10 minutes and then 20 μl of RIPA was added and this sample was utilized for Western blot analysis. The sAPPα bands were detected using the 6E10 (1:1000) as the primary antibody.
Luciferase assays. For firefly luciferase assays, 7,500 cells were plated into the 96 well plates. In an experiment, each treatment was applied to a total of six wells. After treatments, conditioned media was aspirated and discarded. 100 μl
GLB (Glo Lysis Buffer, Promega) was added to lyse the cells. 30 μl of each cell lysates was transferred to a white plate (Greiner Bio-one), and 30 μl Steady-
Glo (Promega) was added. Luciferase was measured using a Top-Count
Scintillation Counter/Luminescence Reader (Packard, Inc.) Another 30ul of each cell lysates was transferred to the other white color plate, and 30 μl 20X
SYBR Green (diluted in PBS from Invitrogen 10,000X SYBR Green) was added.
SYBR Green fluorescence was measured after 5 minutes incubation in dark
65 using an excitation wavelength of 485 nm, and emission wavelength of 527 nm, and an integration time of 0.1 seconds on a Fluoroscan Ascent FL fluorescence plate reader (Thermo Labsystems, Inc.). The luciferase signal was normalized to cell number by dividing the luciferase signal by the SYBR Green reading for the same well. For dual luciferase assays, SH-SY5Y-APP-Gal4 cells that stably express firefly luciferase were co-transfected with pRL-SV40, which constitutively over-expresses Renilla luciferase, along with other plasmids. The dual luciferase assay was performed 24 – 48 hours post-transfection. The media was aspirated and discarded. 30 μl Dual-Glo luciferase substrate
(Promega) was added to lyse the cells. All cell lysates were resuspended and transferred to a white 96 well plate (Greiner Bio-one). After 10 minutes incubation at room temperature, firefly luciferase was measured using a Top-
Count Scintillation Counter/Luminescence Reader (Packard, Inc.). Next, 30μl
Stop-Glo substrate (Promega) was added to the cell lysates containing Dual-
Glo. After 10 minutes room temperature incubation, Renilla luciferase was measured using the same Top-Count Scintillation Counter/Luminescence
Reader (Packard, Inc.) For normalization, the firefly luciferase signal was divided by the Renilla luciferase signal for the same well. In an experiment, each treatment was applied to a total of four wells.
RNA Extraction and Real-time, Quantitative PCR. In triplicate, cells stably expressing control or Ubiquilin 1 specific shRNAs were washed twice with cold
PBS and total RNA was isolated using the RNeasy Mini Kit (Qiagen, Inc). cDNA was synthesized using total RNA (3.5 μg), N6 random primers (12.5 μM) and
SuperScript II Reverse Transcriptase (Invitrogen). cDNAs were diluted 1:30
66 using RNase-free H2O to a final concentration of 2 ng. Diluted cDNAs were mixed with APP or 18S primer/probe sets (Applied Biosystems, Inc.; APP
Catalog # Hs00169098_m1; 18S Catalog # Hs99999901_s1), 2X PCR
Universal Master Mix (Applied Biosystems, Inc.) and amplified using an ABI
7500 Real-Time PCR System following the manufacturer’s directions. To determine differences in APP mRNA levels, we utilized the ΔΔCt method.
Statistical analysis. Values in the text and figures are presented as means ± standard errors of at least three independent experiments. Equal variance or separate variance two-sample student’s t-test were used, as appropriate, to compare two groups. Bonferroni correction analysis was used to correct for multiple comparisons within a single experiment.
67 Competing interests
RET reports being a consultant or serving on the scientific advisory board and board of directors of Torrey Pines Therapuetics and Prana Biotechnology; holding equity or stock options with Torrey Pines Therapuetics, Prana
Biotechnology, and Elan; and having received consulting or lecture fees from
Novartis, Aventis Pharma, Eisai, and PureTech Ventures. No other authors reported any potential conflicts of interest. AJS is a shareholder in Torrey Pines
Therapuetics.
Authors' contributions CZ and PJK designed and carried out the majority of the experiments, data interpretation, and helped draft the manuscript. RC performed QPCR experiments. TLC created the SH-SY5Y-APP-Gal4 stable cell line, performed initial characterization of the AICD-Gal4 luciferase assay. SS established the
AICD-Gal4 assay in 96 well format using Sybr green cell number normalization.
SAP helped in the characterization of Ubiquilin 1. CPC performed the initial characterization of the AICD-Gal4 luciferase assay. JCL was involved in the statistical analyses, interpretation of results and preparation of the manuscript.
RET was involved in the design of the genetic screen and interpretation of results. AJS designed the genetic screen, coordinated the studies, interpreted the results, and drafted the manuscript.
68 Acknowledgements We would like to thank Dr. Thomas Südhof and colleagues generously sharing the APP-Gal4, APP*-Gal4 and Gal4-UAS plasmids; Dr Paul Saftig for generously sharing the ADAM10 plasmid; Dr. Carl Blobel for generously sharing ADAM9 and ADAM17 plasmids; and Dr. Mervyn Monteiro for generously sharing the Ubiquilin 1 plasmid. We would also like to thank all the members of the Saunders lab for helpful discussions and technical assistance in the completion of this work. This work was funded by NINDS (AJS),
Commonwealth of PA and Drexel University.
69 Figures Figure 2-1 - Functional screen for regulators of APP metabolism.
(A) Model depicting APP-Gal4 reporter system. (C) Firefly luciferase activity is significantly increased in SH-SY5Y cells stably expressing APP-Gal4 and Gal4-
UAS Luciferase compared to SY5Y cells stably expressing either Gal4 / Gal4-
UAS Luciferase or APP*-Gal4 / Gal4-UAS Luciferase. Luciferase activity was normalized to total cell number using SYBR Green. Bars represent mean normalized luciferase expression (+/- std. error) of 16 independent trials for each cell line. ** p < 0.01; Student's t-tests with sequential Bonferroni correction for multiple comparisons.
Figure 2-2 - Pharmacological modulation of secretases alters AICD-Gal4 levels and AICD-Gal4 mediated luciferase activity in SY5Y-APP-Gal4 cells.
(A) Stimulation of α-secretase by PMA (1 μM PMA for 10 hours) increases sAPPα, C83-Gal4, and AICD-Gal4 levels as detected by Western blot analysis.
(B) Quantification of Western blot densitometry in panel A. Normalization for loading differences was achieved by dividing the densitometry values for individual bands by the densitometry values for β-actin in the same lane. (C)
Dose-dependent increases of AICD-Gal4- mediated luciferase activity with increasing concentrations of PMA (10 hour incubation). Luciferase levels normalized to total cell number using protein concentration. (D) Inhibition of α- secretases by TAPI-1 (20 μM for two hours) results in decreases in sAPPα,
C83-Gal4, and AICD-Gal4 levels as detected by Western blot analysis. (E)
Quantification of Western blot densitometry in panel D. (F) Dose-dependent decreases in AICD-Gal4-mediated luciferase activity with increasing TAPI-
70 1concentrations (two hour incubation). Luciferase levels normalized to total cell number using SYBR Green. (G) Inhibition of γ-secretase by L-685,458 (5mM) decreases AICD-Gal4 levels and increases C83-Gal4 levels as detected by
Western blot analysis. (H) Quantification of Western blot densitometry in panel
G. (I) Dose-dependent decreases in AICD-Gal4-mediated luciferase activity with increasing concentrations of L-685,458. For the luciferase experiments, points represent mean normalized luciferase activity (+/- standard error) of three independent trials, with luciferase levels normalized to total cell numbers using SYBR Green. ** p < 0.01; Student's t-tests with sequential Bonferroni correction for multiple comparisons. “Control” uses the same media as the treatments, and also contains the same amount of DMSO.
Figure 2-3 - Over-expression of individual secretase genes in SY5Y-APP-
Gal4 cells increases AICD-Gal4 mediated luciferase activity.
(A) Transient over-expression of ADAM10 increases ADAM10, AICD-Gal4,
C83-Gal4, and sAPPα levels compared to cells transfected with empty vector.
(B) Quantification of Western blot densitometry in panel A. (C) ADAM17 transient over-expression significantly increases ADAM17, AICD-Gal4, C83-
Gal4, and sAPPα levels. (D) Quantification of Western blot densitometry in panel G. (E) Transient over-expression of individual secretase genes increases
AICD-Gal4 mediated luciferase activity. Luciferase was normalized to transfection efficiency, by dividing by Renilla luciferase activity. Individual secretase over-expression plasmids were co-transfected with pRL-SV40 plasmid, expressing Renilla luciferase. Bars represent the mean normalized luciferase activity of four independent trials and error bars represent standard
71 errors. Statistical significance was determined using two-sample, one-tail t-tests to compare each secretase gene with the empty vector, followed by sequential
Bonferroni procedure to adjust for multiple comparisons. *p<0.05; ** p<0.01
Figure 2-4 - Knock-down of APP and individual secretase genes in SY5Y-
APP-GAL4 cells decreases AICD-Gal4 mediated luciferase activity.
(A) APP-specific shRNA decreases full-length APP, C83-Gal4, and AICD-Gal4 levels compared to the control or “non-silencing” shRNA. Results from duplicate transfections with each shRNA are shown. (B) ADAM10 specific shRNAs decrease endogenous ADAM10, C83-Gal4, and AICD-Gal4 levels compared to the control shRNA. Results from duplicate transfections with each shRNA are shown. (C) ADAM17 specific shRNAs decrease endogenous ADAM17, C83-
Gal4, and AICD-Gal4 levels compared to the control shRNA. Results from duplicate transfections with each shRNA are shown. (D) Knock-down of
ADAM9, 10, and 17 decrease sAPPα levels compared to control shRNA. (E)
Quantification of Western blot densitometries in panels B – D. (F) Transfection with shRNAs specific for APP and individual secretase genes decreases AICD-
Gal4-mediated luciferase expression compared to control shRNA. Bars represent the mean normalized luciferase activity of four independent trials and error bars represent standard errors. Statistical significance was determined using two-sample, one tailed t-tests to compare each secretase shRNA with the control shRNA and sequential Bonferroni procedure to adjust for multiple comparisons. *p<0.05; ** p<0.01.
Figure 2-5 - Genetic alteration of Fe65 or Tip60 levels modulates AICD-
Gal4 mediated luciferase activity.
72 (A) Transient over-expression of Tip60 or Fe65 in SY5Y-APP-Gal4 cells increases AICD-Gal4 production compared to empty vector controls. (B) Knock- down of Fe65 or Tip60 in SY5Y-APP-Gal4 cells decreases AICD-Gal4 mediated luciferase activity Bars represent the mean normalized luciferase activity of four independent trials and error bars represent standard errors.
Statistical significance was determined using two-sample, one-tailed t-tests to compare each secretase gene and "vector" or "control" and sequential
Bonferroni procedure to adjust for multiple comparisons. * p<0.05.
Figure 2-6 - Ubiquilin 1 knock-down regulates APP-Gal4 metabolism in
SY5Y-APP-Gal4 cells.
(A) Ubiquilin 1 knock-down decreases AICD-Gal4-mediated luciferase activity.
SY5Y-APP-Gal4 cells stably expressing the control shRNA, an APP specific shRNA, or five different shRNA targeting Ubiquilin 1 was generated. Cell lysates were utilized to measure AICD-Gal4 mediated luciferase activity. Bars represent the mean normalized luciferase activity (+/- standard error) of six independent trials. (B) SY5Y-APP-GAL4 cells stably expressing Ubiquilin 1 specific shRNA (#2) have decreased Ubiquilin 1, mature and immature APP-
Gal4, C83-Gal4, AICD-Gal4, and sAPPα levels compared to cells expressing control shRNA. (C) Ubiquilin 1 knock-down does not alter APP mRNA levels compared to control shRNA using quantitative PCR. (D) Ubiquilin 1 knock-down decreases PS1-CTF levels.
Figure 2-7 - Ubiquilin 1 over-expression regulates APP-Gal4 metabolism in SY5Y-APP-Gal4 cells.
(A) Transient Ubiquilin 1 over-expression increases AICD-Gal4 mediated luciferase activity. SY5Y-APP-GAL4 cells were transiently co-transfected with
73 UBQLN1 over-expression plasmid and a Renilla luciferase over-expression plasmid (pRL-SV40). The latter was used as a transfection efficiency control to normalize AICD-Gal4 mediated luciferase activity. Bars represent the mean normalized luciferase activity (+/- standard error) of six independent trials.
Statistical significance was determined using two-sample, one-tailed t-tests to compare each experimental shRNA to the control shRNA and sequential
Bonferroni procedure to adjust for multiple comparisons. (B) SY5Y-APP-Gal4 cells transiently over-expressing Ubiquilin 1 have increased Ubiquilin 1, mature and immature APP-Gal4, C83-Gal4, AICD-Gal4, and sAPPα levels compared to vector only cells. (C) Ubiquilin 1 over-expression increases PS1 CTF levels in SY5Y-APP-Gal4 cells.
Figure 2-8 - Quantification of Western blot densitometries from Figures 6
& 7.
Bars represent mean densitometry (+/- standard error) of three independent trials. White bars represent the densitometry from Ubiquilin 1 knock-down cells; gray bars represent the densitometry from control cells (either control shRNA or empty vector for knock-down and over-expression respectively); black bars represent the densitometry from Ubiquilin 1 over-expressing cells.
Abbreviations: Ma APP denotes mature APP, Im APP denotes immature APP;
Ma/Im APP denotes the mature APP/ immature APP ratio. Statistical significance between mock and over-expression for each measure was determined using a two-sample, one tailed t-test and sequential Bonferroni procedure to adjust for multiple comparisons. (* p<0.05; **p<0.01.)
Figure 2-9 - Correlation between AICD-Gal4 mediated luciferase levels and
AICD-Gal4 levels determined by Western blot analysis.
74 (A) Using data from pharmacologic (PMA, TAPI-1, L-685,458), over-expression
(ADAM 10, ADAM17, Ubiquilin 1) or knock-down (ADAM 10, ADAM17,
Ubiquilin 1) mediated modulation of AICD-Gal4 levels we plotted the % change in AICD-Gal4 levels versus the % change in AICD-Gal4 mediated luciferase activity. For transient over-expression, luciferase activity and AICD-Gal4 levels were normalized to transfection efficiency by Renilla luciferase activity assays.
The line is represents the linear regression to this data.
Table
Table 2-1 – Z-factor values
Z-factor values for AICD-Gal4 Luciferase assay calculated when APP metabolism is modulated by pharmacologic or genetic approaches.
75 Additional files Files for each of nine figures
APP Metabolism Modulators Z factor
PMA 0.77
Pharmacological TAPI-1 0.63
L685,458 0.74
APP 0.60
shRNA ADAM10 0.71 ADAM17 0.70
Over-expression ADAM10 0.72
ADAM17 0.60
Table 2-1. Z-factor values.
1A
1B
Figure 2-1: Functional screen for regulators of APP metabolism.
77 2A
2B
2C
78 2D
2E
2F
79 2G
2H
2I
Figure 2-2: Pharmacological modulation of secretases alters AICD-Gal4 levels and AICD-Gal4 mediated luciferase activity in SY5Y-APP-Gal4 cells.
80 3A
3B
81 3C
3D
82 3E
Figure 2-3: Over-expression of individual secretase genes in SY5Y-APP-
Gal4 cells increases AICD-Gal4 mediated luciferase activity.
83 4A
4B
84 4C
4D
4E
85 4F
Figure 2-4: Knock-down of APP and individual secretase genes in SY5Y-
APP-GAL4 cells decreases AICD-Gal4 mediated luciferase activity.
86 5A
5B
Figure 2-5: Genetic alteration of Fe65 or Tip60 levels modulates AICD-
Gal4 mediated luciferase activity.
87 6A
88 6B
89 6C
6D
Figure 2-6: Ubiquilin 1 knock-down regulates APP-Gal4 metabolism in
SY5Y-APP-Gal4 cells.
90 7A
91 7B
7C
Figure 2-7: Ubiquilin 1 over-expression regulates APP-Gal4 metabolism in
SY5Y-APP-Gal4 cells.
92 8A
8B
Figure 2-8 - Quantification of Western blot densitometries from Figures 6
& 7.
93
Figure 2-9 - Correlation between AICD-Gal4 mediated luciferase levels and
AICD-Gal4 levels determined by Western blot analysis.
94
Figure 2- 10: APP-Gal4 fusion protein architecture.
95
ORIGIN: APP695-Gal4; total 938 amino acids
1 mlpglallll aawtvwalev ptdgnaglla epqiamfcgr lnmhmnvqng kwdsdpsgtk 61 tcidtkegil qycqevypel qitnvveanq pvtiqnwckr grkqckthph fvipyrclvg 121 efvsdallvp dkckflhqer mdvcethlhw htvaketcse kstnlhdygm llpcgidkfr 181 gvefvccpla eesdnvdsad aeeddsdvww ggadtdyadg sedkvvevae eeevaeveee 241 eadddedded gdeveeeaee pyeeatertt siattttttt esveevvrvp ttaastpdav 301 etpgde nehahfqkak erleakhrer msqvmrewee aerqaknlpk adkkaviqhf 361 qekvesleqe aanerqqlve thmarveaml ndrrrlalen yitalqavpp rprhvfnmlk 421 kyvraeqkdr qhtlkhfehv rmvdpkkaaq irsqvmthlr viyermnqsl sllynvpava 481 eeiqdevdel lqkeqnysdd vlanmisepr isygndalmp sltetkttve llpvngefsl 541 ddlqpwhsfg adsvpanten evepvdarpa adrglttrpg sgltniktee isevkmdaef 601 rhdsgyevhh qklvffaedv gsnkgaiigl mvggvviatv ivitlvmlkk kplassr mkl 661 lssieqa cdicrlkklk cskekpkcak clknnwecry spktkrsplt rahltevesr ler 721 leqlfll ifpredldmi lkmdslqdik alltglfvqd nvnkdavtdr lasvetdmpl tlr 861 qhrisat ssseessnkg qrqltvs pefpgippgqytsihhgv vevdaavtpe erhlskmq 921 qn gyenptykff eqmqn
Figure 2-11: APP-Gal4 fusion protein amino acids sequence. Regular
characters are APP695 sequence; bold and italic characters are linker sequence;
underlined characters are from Gal4 sequence. APP695-Gal4 contains total of
938 amino acids.
96
Figure 2-12: Screening of APP metabolism regulator using an AICD- mediated functional assay.
97
Figure 2-13: 11 genes were identified as APP metabolism regulators using our validated AICD-mediated functional assay.
98 CTSL: Cathepsin Potential contribution of the lysosomal compartment in L the processing of amyloid precursor protein (APP) to amyloid beta-peptides CTSL2: A cysteineprotease; proteolysis and peptidolysis; Cathepsin L2 cathepsin L activity. FRMD3: FERM A multifunctional protein essential for maintaining domain erythrocyte shape and membrane mechanical containing 3 properties. NTRK2: Receptor for brain-derived neurotrophic factor (BDNF), Neurotrophic neurotrophin-3 and neurotrophin-4/5; involved in the tyrosine kinase, development and/or maintenance of the nervous receptor, type 2 system. Indicated to be an AD suspitible gene. In vivo protein degradation, modulates accumulation of UBQLN1: presenilin proteins, and is found in lesions associated Ubiquilin 1 with Alzheimer's and Parkinson's disease.
Table 2-2: List of genes that their encoded proteins down-regulation decreases AICD-medicated luciferase and are considered positive APP metabolism regulators.
99
KIF27: kinesin family Kinesin motor member 27 Homophilic cell adhesion molecule that promotes axonal growth. May play a role in nerve regeneration NINJ1: Ninijurin1 and in the formation and function of other tissues; neurogenesis NR4A3: nuclear Neuron-derived orphan receptor; Mitogen-induced receptor subfamily nuclear orphan receptor 4, group A, member 3 PPP3R2: Protein Regulatory subunit of calcineurin, a calcium- phosphatase 3 dependent, calmodulin stimulated protein (formerly 2B), phosphatase. calcium ion binding; MAPK signaling regulatory subunit B, pathway; Apoptosis; Long term memory beta isoform SPTLC1: serine Catalytic activity: Palmitoyl-CoA + L-serine = CoA + palmitoyltransferase, 3-dehydro-D- sphinganine + CO(2). Belongs to the long chain base class-II pyridoxal-phosphate-dependent subunit 1 aminotransferase family. SHC3: Src Signaling adapter that couples activated growth homology 2 domain- factor receptors to signaling pathway in neurons. containing signal transduction pathways of neurotrophin- transforming protein activated Trk receptors in cortical neurons C3 Tropomodulin is highly concentrated at the postsynaptic domain of human and rat TMOD1: neuromuscular junctions; tropomyosin binding; Tropomodulin 1 cytoskeleton; organization and biogenesis; actin binding
Table 2-3: List of genes that their encoding proteins down-regulation increases
AICD-medicated luciferase and are considered negative APP metabolism regulators.
100
CHAPTER 3
Functional characterization of APP metabolism
Regulators
CHAPTER 3.1 Ubiquilin 1 and protein quality control system
Can Zhanga, Aleister J. Saundersa,b
c- Department of Bioscience & Biotechnology, Drexel University, Philadelphia, PA d- Department of Biochemistry & Molecular Biology, Drexel University College of Medicine, Philadelphia, PA
Abstract: The refolding and degrading misfolded proteins are the
most important function of protein quality control (PQC) system. PQC
essential activities occur in the endoplasmic reticulum (ER), ubiquitin-
proteasome system (UPS) and lysosome. Imbalance between the
capability of PQC system and the quantity and severity of misfolded
proteins may cause protein aggregate to accumulate and may ultimately
contribute to a class of diseases referred to as conformational disorders.
Numerous lines of evidence suggest that Ubiquilin 1 is an important component in PQC. Ubiquilin 1 has been indicated to be involved in the pathophysiology of neurodegenerative diseases and cancer. A number of
101 Ubiquilin 1 interacting proteins have been identified and it seems that
Ubiquilin 1 functions are not exclusively limited to what is classically
defined as PQC functions. These results also implicated that Ubiquilin 1
is important in the transcription and translation.
Introduction
Soluble and transmembrane proteins undergo complex and precise folding to ensure they are in the physiologically correct conformation. The system that ensures correct folding is called protein quality control (PQC) system. The most important intracellular compartments that harbor PQC activities are endoplasmic reticulum (ER), ubiquitin-proteasome system (UPS) and lysosome[150-153]. In the normal physiological state in eukaryotes, as many as 30% newly synthesized proteins are indicated to undergo degradation within minutes of synthesis[151,
154], which implies that these degraded proteins could be misfolded[155].
Misfolded proteins could first be recognized by the ER and retrotranslocated into the cytosol, or utiquitinated and degraded by the proteasome system or degraded by the lysosome[152]. Protein chaperones, e.g. Hsp70, Hsp90, Hsp40, and Hsp104, reside in every major cellular compartment and can facilitate PQC functions and specifically refold or degrade misfolded proteins[156, 157].
Imbalance between the capability of PQC system and the quantity and severity of misfolded protein may cause protein aggregate to accumulate. The proteins that can not be degraded tend to have more severe intracellular toxicity.
The intracellular organelle that contains such proteins is called aggresome or
102 inclusion body[158-160], which is considered a temporary protective mechanism
to confine toxicity[161]. The rapid dividing cells can relieve the toxicity by passing onto daughter cells, however, the non-dividing neuron is more susceptible to protein aggregates[153]. Therefore, disorders of protein folding and degradation are emerging as a fundamental mechanism for many diseases, especially neurodegenerative diseases [162] . The proteins that deliver misfoled proteins to the proteasome include, at least in some cases, the chaperone protein
CDC48/p97[163, 164], and a recently identified protein, Ubiquilin 1. It appears that Ubiquilin 1(in human), or Dsk2p (in yeast), fulfill these responsibilities, as well as carry out other biological functions. In this review, we summarize the current findings of Ubiquilin 1 inside and outside of PQC system, and we will discuss some clinical disorders derived from the alteration of Ubiquilin 1.
ER, UPS and autophagy-lysosome system
The ER is the most important eukaryotic organelle for protein folding and degradation. It contains a highly active folding machinery to fold the proteins from an unfolded state to their native state and enter the secretory pathway. The ER also recognizes misfolded proteins through the unfolded protein response (UPR) and targets them for elimination by a mechanism called ERAD or ERQD ( ER- associated quality control and degradation) [156, 165]. The misfolded proteins recognized by the ERQD will be degraded through the ubiquitin-proteasome system (UPS) or lysosome.
The UPS system is essential for many cellular processes, including the cell cycle, the regulation of gene expression, and responses to oxidative stress.
103 In the UPS, misfolded proteins are first covalently labeled with ubiquitin, a small
conserved protein, through the process of ubiquitination or ubiquitinylation. It is
an ATP-dependent process that involves the action of at least three enzymes: a
ubiquitin-activating enzyme (E1), a ubiquitin-conjugating enzyme (E2), and a
ubiquitin ligase (E3), which work sequentially in a cascade[151, 152].
Ubiquitinylation is an important regulatory tool that controls the concentration of
key signaling proteins, such as those involved in cell cycle control, as well as
removing misfolded, damaged or mutant proteins that could be harmful to the cell.
Several clinical syndromes are caused by disruption of the genes that encode
enzymes for ubiquitinylation, for instance, Angelman syndrome (caused by
mutation from the gene UBE3A which encodes the protein ubiquitin protein ligase
E3A; or larger deletion of chromosome 15 regions) and Von Hippel-Lindau (VHL)
syndrome (caused by mutations of the VHL tumor suppressor (VHL) gene which encodes the VHL protein). Abnormal ubiquitinylation of proteins could results in intracellular accumulations, which are called inclusion bodies or aggresomes, as were mentioned previously. Examples of such inclusions bodies include neurofibrillary tangles (Alzheimer’s disease), Lewy body (Parkinson’s disease),
Pick bodies (Pick’s disease) and Mallory’s Hyalin (alcoholic liver disease).
After a polyubiquitinated chain is formed, the proteins will be delivered to the proteasome specifically for degradation (Figure 1). Structurally speaking the proteasome is a large barrel-like complex about 2000 kDa in molecular mass which contains a "core" of four stacked rings around a central pore and two regulatory caps on both ends. The core is where proteins are degraded and the
104 regulatory subunit contains multiple ATPase active sites and ubiquitin binding
sites. The most common form of the proteasome is known as the 26S
proteasome containing one 20S core and two 19S regulatory caps. The
alternative forms of the proteasome can be the 20S core with two 11S regulatory
caps or with two mixed caps (one 19S on one cap and one 11S of the other cap).
Larger misfolded proteins or organelle will be degraded by the lysosome
through hydrolytic reactions in its acidic environment. The function of its degrading intracellular components is called autophagy, which is different from its function of degrading exterior materials, or heterophagy (e.g. exterior antigens are presented through heterophagy) (Figure 1). In general lysosome degrades protein with much less specificity than the UPS. However, during a process called chaperone medicated autophagy (CMA), chaperones (particularly heat-
shock proteins) selectively bind and transport substrate proteins with the
sequence of KFERQ to lysosomal receptor (LAMP-2A) and proteins are
endocytosed into the lysosome. Disorder of autophagy can result in intracellular
protein aggregates and lead to several neurodegenerative diseases, including
Alzheimer’s disease (AD), Pakinsen’s disease (PD), and polyglutamine diseases[153].
105
Figure 3-1A Protein quality control systems in mammalian cells.
Proteins degraded through ubiquitin-proteaseom system (UPS) or lysosome system, the two essential components of protein quality control (PQC). Proteins are delivered to lysosomes from the extracellular media (heterophagy) or from inside the cell (autophagy). Mammalian cells carry out three different types of autophagy: macroautophagy, microautophagy, and chaperone-mediated autophagy (CMA). Reproduced from the Lancet Neurology, 6 (4), Pages 352-361
(2007)
Ubiquilin family of proteins
The human genome encodes four structurally related proteins: Ubiquilin 1,
2, 3 and 4. Their encoding genes are located on different chromosome regions, and contain different length of amino acids. The Ubiquilin 1 gene is located on
106 chromosome 9q22, and it can encode two isoforms containing either 589 amino acids or 561 amino acids. The Ubiquilin-2 gene is located on chromosome Xp11 and it encodes a protein of 624 amino acids. The Ubiquilin-3 gene is located on chromosome 11p15, and it encodes a protein of 655 amino acids. Ubiquilin-4 is located on chromosome 1q21, and it encodes a protein of 601 amino acids[166].
All the Ubiquilin family proteins are cytosolic proteins with different expression patterns. Ubiquilin 1 is expressed ubiquitously and it can be phosphorylated [167],
Ubiquilin-2 and 4 are expressed more tissue specifically than Ubiquilin 1, and
Ubiquilin-3 is expressed only in the testis. The four proteins differ from each other primarily by the presence or absence of long insertions in the middle of the sequence. Ubiquilin 1 is also known as PLIC1, CHAP1, DA41, DSK2(yeast),
FLJ90054, and XDRP1(frog) [125] . Ubiquilin2 is also known as CHAP1, DSK2,
HRIHFB2157, LIC-2, N4BP4, PLIC2, and RIHFB2157.
Ubiquilin proteins are structurally conserved protein and contain an ubiquitin-like domain (UBL) in its N-terminus and an ubiquitin-associated domain
(UBA) in its C-terminus. Functionally Ubiquilin 1 interacts with many cytosolic or transmembrane proteins using its functional domains and modulates their stability and/or steady state levels. Ubiquilin 1 can form dimmers, though the monomer is indicated to be involved in binding one category of its substrate, presenilins [168]. The rest of this review will delineate the Ubiquilin 1 involvement in the protein intracellular quality control system, as well as explore Ubiquilin 1 involvement in various disorders.
Ubiquilin 1 and protein intracellular quality control systems
107 1. Ubiquilin 1 and ER Yeast has been used to study the proteins that are required for ERQD due to the ease of genetic and molecular manipulation. The
Ubiquilin 1 homologue in yeast, Dsk2p, has been implicated in the ERQD [155,
169] [170]. The results from these studies suggested that Dsk2p, as well as two other proteins, Rad23p and Cdc48, form a trimeric complex and function to deliver ubiquitinated proteins to the proteasome. This complex prevents misfolded protein aggregates from developing in the cytoplasm [170].
2. Ubiquilin 1 and lysosome Autophagy activities have been shown to be regulated[153], therefore, enhancing the autophagy activities could be a therapeutic for the autophagy associated disorders. Rapamycin (also named as sirolimus or INN) was first discovered as a product of the bacterium
Streptomyces hygroscopicus and was originally developed as an antifungal agent, and now used as agents of anti-rejection. Recently it was found that rapamycin is pro-autophagic. It can activate the functions of autophagy and increase the clearance of aggregated proteins in polyglutamine mutant model cells by inhibiting mTOR (mammalian target of rapamycin) [171]. mTOR is known to control cell cycle progression and cell growth through regulation of translation, transcription, membrane traffic and protein degradation. It is reported that Ubiquilin 1 interacts directly with mTOR protein kinase [172]. Therefore it is implicated that autophagy activity could be regulated by Ubiquilin 1 via its interaction with mTOR.
3. Ubiquilin 1 and its protein transportation properties A large amount of evidence shows that Ubiquilin 1 functions as an adaptor protein and links the
108 ubiquitination machinery to the proteasome to affect protein degradation [173].
Specifically Ubiquilin 1 mediates degradation of protein-disulfide isomerase (or
PDI; a stress-responsible gene)[174], [175], neuronal nicotinic acetylcholine
receptors[144] and presenilins (γ-secretase component; involved in APP
proteolytic cleavage). It appears that Ubiquilin 1 can also enhance the
polyubiquitination of NS5B (a crucial protein involved in HCV RNA transcription)
[148]. If misfolded proteins exceed the degradation capacity of proteasome,
Ubiquilin 1 can also target certain proteins to form aggresome with unidentified
mechanism. Such examples include ataxin 3 (a deubiquitinating enzyme ),
HSJ1a (a co-chaperone), and EPS15 (epidermal growth factor substrate 15; an endocytic protein)[173] [143].
Other Ubiquilin 1 binding proteins
Ubiquilin 1 has been found to interact directly with several other proteins
that play various functions. Currently there is no direct evidence showing the
Ubiquilin 1 can enhance polyubiquitination or deliver these proteins to the
proteasome. These proteins include Eps15 and Eps15R (key components of the
clathrin-mediated endocytic pathway) [176], mTOR (regulating cell cycling and
cell growth through regulation of translation, transcription, membrane traffic and
protein degradation) [172], gamma-aminobutyric acid A (GABA(A)) receptors
(modulating efficacy of inhibitory neurotransmission) [147], human achaete-scute homologue-1(HASH-1; essential for development of olfactory and most peripheral autonomic neurons) [146], CXCR-4-βγ (a G-protein coupled receptor –
βγ units, receptor for stromal cell-derived factor1, or SDF-1, also named as
109 CXCL12 which is involved in angiogenesis), CD47 (modulator of integrin function and cell migration) [177]. Interestingly it has been indicated that Ubiquilin 1 functions on SDF-1 are independent on the proteasome[177].
Ubiqulin 1, protein quality control and diseases
1. Ubiquilin 1 and Alzheimer’s disease AD is a progressive and
degenerative disorder clinically characterized by progressive dementia that
inevitably leads to incapacitation and death. AD pathology is characterized by the
presence of intraneuronal fibrillary tangles and extracellular senile plaques.
Tangles are mainly composed of microtubule associated protein tau in its
hyperphosphoralation form. Plaques are primarily composed of the 4 kDa, 39–43
amino acids Aβ and The Aβ peptide is generated from a transmembrane protein
β-amyloid precursor protein (APP)[14]. Abundant genetic, cell biological and
biochemical evidence supports the amyloid cascade hypothesis which states that
accumulation and aggregation of Aβ is the primary cause of AD, inducing an
inflammatory response followed by neuritic injury, hyperphosphorylation of tau
protein and formation of fibrillary tangles, leading ultimately to neuronal
dysfunction and cell death[1, 16, 178, 179].
Four genes have been confirmed to be involved in AD, including APP,
PSEN1, PSEN2, and ApoE[16, 75, 180]. Numerous genes have been suggested
to be involved in AD pathophysiology and their functions and underlying
mechanisms need to be confirmed. Evidence shows that Ubiquilin 1 is involved in
neurodegenerative diseases, including AD, PD, and HD. First on the genetic
level, Bertram et al reported that a UBQLN1 polymorphism substantially
110 increases the risk of AD, possibly by influencing alternative splicing of this gene
in the brain [137, 181]. Second, on the immunohistochemistry level using anti-
Ubiquilin antibodies robustly stained neurofibrillary tangles and Lewy bodies in
AD and PD affected brains, respectively[125]. Third, functionally, Ubiquilin 1
regulates γ-secretase (the APP cleavage proteases which can produce Aβ)
activity by regulating endoproteolysis of the presenilin 1 protein within the γ-
secretase complex [125, 127, 182]. Fourth on the imaging level, using
fluorescence lifetime imaging microscope, the interaction between Ubiquilin 1
and presenilin1 was detected near the cell surface in primary neurons in vitro as
well as in brain tissue of healthy controls and AD patients [183]. Massey et al
reported Ubiquilin 1 and PS proteins co-localized in vesicular-like structure or
ER- like pattern [125, 127]. Ubiquilin 1 affects APP trafficking and processing,
thereby influencing the generation of Aβ [124].Taken together, Ubiquilin 1 is
involved in the AD pathophysiology and may be also involved in the
pathogenesis. It is an important modulator of presenilin protein accumulation and
APP trafficking.
2. Ubiqulin and polyglutamine diseases Polyglutamine (PolyQ) diseases are a
category of neurodegenerative diseases characterized by expanded polyQ tracts.
Based on clinical features PolyQ diseases are classified of nine different types,
e.g. Huntington’s disease (HD) and spinocerebellar ataxia type1 (SCA1).
However they seem to share the same underlying mechanisms which are the expansion of a CAG trinucleotide repeat and the breakdown of the PQC system[184]. Protein aggregation and cytotoxicity are observed in polyQ
111 disorders. Recent evidence suggested that Ubiquilin family proteins are involved in the pathophysiology of PolyQ diseases. SCA1 (previously also known as olivopontocerebellar atrophy type 1) is a genetic disorder clinically characterized by slowly progressive incoordination of gait and often associated with poor coordination of hands, speech, and eye movements. The disease causing gene
Ataxin-1 (or ATX1, SCA1), is located on the chromosome 6p23 and encodes a protein called Ataxin-1[185], which is shown to interact with Ubiquilin-4 ( or ataxin-1-interacting proteins; A1UP) [186]. The mechanism of the interaction is still unidentified. HD is another genetic disorder characterized by mutations from the Huntingtin (Htt) gene located on the chromosome 4p16. Mutant Huntingtin protein (mHtt) forms nuclear inclusions and neuropil aggregates and results in
neuronal cell death in select areas of the brain, which causes abnormal body movements called chorea and lack of coordination. Wang et al reported that
Ubiquilin 1 suppresses polyQ-induced protein aggregation and toxicity in cells and in an animal model of HD [187].
3. Ubiquilin 1 and cancer Cancer is characterized by uncontrolled
division of cells. Cyclins and cyclin-dependent kinases (CDKs) are the two
critical classes of molecules in regulation of cell cycle progression. Funakoshi
reported that in Xenopus cells, XDRP1, the Ubiquilin 1 homologue, bound to both
embryonic and somatic forms of cyclin A (A1 and A2) in Xenopus cells and
blocked embryonic cell division [188]. Ubiquilin 1 (named as DA41 in this report)
can interact with tumor-suppressor DAN protein and S (1-5) protein, which can
modulate DNA synthesis, thus can regulate cell growth [189]. A recent report
112 showed that Ubiquilin 1 is involved in cancer pathophysiology. Ubiquilin 1 mRNA and protein levels are both significantly increased, and the phosphorylated form is significantly reduced in lung adenocarcinoma [167] . Taken together, Ubiquilin
1 is involved in cancer pathophysiology and possibly cancer pathogenesis.
Conclusion
In summary, the protein control system is a complex intracellular system which refolds and degrades misfolded cytosolic and transmembrane proteins.
The main components in the PQC include ER, UPS and lysosome. First stages of PQC occur in the ER where refolding of misfolded proteins occurs through heat shock proteins and other chaperone proteins. UPS and lysosome seem to be the second stage of quality control. The proteins which can not be repaired in the ER will undergo UPS or lysosome. To maintain normal cellular functions, these three systems need to coordinate their functions. Ubiquilin 1 is a key adaptor protein that seems to be involved in ER, UPS and lysosome functions.
Ubiquilin 1 binds a larger amount of proteins that function differently. It seems that Ubiquilin 1 can function independently of the proteasome. Imbalance of the
PQC system will lead to misfolded proteins aggregation, and ultimately lead to apoptosis or cell death. Malfunctioning of PQC is an emerging mechanism for many disorders, including neurodegeneraton diseases and cancer. Modulation of
Ubiquilin 1 activity may open up novel avenues for the intervention of these disorders.
113
CHAPTER 3.2
Characterization of Ubiquilin 1 mediated APP
metabolism and its interaction with the proteasome
system
Can Zhang1, Jeff Thomas2, Gregg Johannes2, He Zhao3, Bahrad A. Sokhansanj3, Rob Moir 4, Aleister J. Saunders1, 5, 6, §
1Department of Bioscience & Biotechnology, Drexel University, Philadelphia, PA 2Department of Pathology, School of Medicine, Drexel University, Philadelphia, PA 3Department of Biomedical Engineering, Drexel University, Philadelphia, PA 4Genetics and Aging Research Unit, MassGeneral Institute for Neurodegenerative Diseases (MIND), Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 5Department of Biochemistry & Molecular Biology, Drexel University College of Medicine, Philadelphia, PA 6Dept. of Neurobiology & Anatomy, Drexel University College of Medicine, Philadelphia, PA
§Corresponding author
114
Introduction
Alzheimer’s disease (AD) is pathologically characterized by accumulation
of neuronal amyloid plaques and fibrillary tangles, which ultimately lead to
neuronal death and dementia. The main components of the amyloid plaques are beta-amyloid peptides (or Aβ), which are 37-43 amino acid long and generated by the proteolytic processing of a type I transmembrane glycoprotein, amyloid-precursor protein (APP)[1, 14]. APP is present on almost every membrane containing intracellular compartments. The process that APP is transported onto the membrane surface after translation is called APP trafficking. Amyloidogenesis of APP requires two proteolytic steps involving the
β-secretase (BACE) and γ-secretase. BACE is also a type-1 transmembrane protein. γ-Secretase is a membrane protein complex which contains presenilin1
(PS1), nicastrin (NCSTN), Aph1α, Pen-2 and CD147. γ-Secretase cleavage can yield different length of Aβ species, among which Aβ40 and Aβ42 are the two main components. More Aβ42 can be found in the amyloid plaques. APP can also be cleaved by α-secretase, which cleaved within the Aβ sequence and preclude the process of amyloidgenesis. Accumulative evidences indicate that
Aβ is the primary cause of the neuronal death for most of AD cases, though the underlying mechanism is not completely identified [23]. Characterizing how
APP and Aβ metabolism is altered in the AD pathogenesis will help elucidate the underlying mechanism of AD.
115 Recently a polymorphism of Ubiquilin 1 has been shown to increase the risk of AD in family-based and large case-control samples[137] [138, 181].
Ubiquilin1 has been implicated to be involved in the pathological process of
Alzheimer’s disease, Parkinson’s disease and cancers[125, 167, 187].Ubiquilin1 gene is located on chromosome 9q22, and it can encode two isoforms of protein that contains 589 amino acids (isoform1) or 561 amino acid (isoform2). Ubiquilin
1 contains a UBL (ubiquitin-like) domain and a UBA (ubiquitin-associated) domain. Utilizing these functional domains, Ubiquilin1 interacts with more than 10 intracellular or surface proteins and modulate their stability and/or steady state levels[125, 143, 144, 147, 172, 174-177]. Ubaln1 has also been reported to be involved in the protein intracellular quality control utilizing including ER and ubiquitin-proteasome system (UPS)[125, 170, 175, 190]. Ubiquilin 1 appears to be an adaptor protein, which binds polyubiquitinated proteins with its UBL or UBA domain and transport them to the proteasome for degradation, thus preventing aggregates of misfolded protein in the cytoplasm [173], [170, 190]. It is postulated that Ubiquilin 1 functions could also be independent on the UPS[177]. In this case, blocking proteasome functions using MG132 or other proteasome inhibitors could not inhibit Ubiquilin 1 influences on the target proteins. Ubiquilin 1 plays a pivotal role in the APP metabolism. First, it is demonstrated that Ubiquilin 1 alters
APP steady state levels on a post-transcriptional level by altering APP trafficking pathway [124]. Secondly, Ubiquilin 1 can regulate PS1 endoproteolysis [127]. We have shown that Ubiquilin 1 modulates APP metabolism and PS1 endoproteolysis in a cell type specific manner. Here we study the effect of
116 Ubiquilin 1 on APP metabolism in SH-SY5Y cells and determine if these functions are dependent on UPS.
Since apoptosis plays a critical role in the development of AD[191] and
Ubiquilin 1 could alleviate the hypoxia induced apoptosis in SH-SY5Y cells[175], we also investigated the role of Ubiquilin 1 role of apoptosis.
In this paper we functionally characterize the Ubiquilin 1 role on APP metabolism in SH-SY5Y cells, followed by the study of the Ubiquilin 1 influence on caspase-3 activity, as well as one of caspase-3’s substrates, PARP. We find that Ubiquilin 1 alters APP trafficking to the cell surface without altering its half life.
We also show that Ubiquilin 1 effects on APP metabolism are dependent on proteasome system. Ubiquilin 1 alters caspase-3 activity and PARP protein levels under stress. Next we demonstrate that Ubiquilin 1 undergoes proteasome and lysome degradation. Finally we find that γ-secretase inhibition can elevate
Ubiquilin 1 steady state levels, which suggests for the first time that there exists a regulatory circuit modulating the Ubiquilin 1 and PS1 activities.
117
Methods
Chemical and antibodies: L-685,458 and puromycin were purchased from
Sigma. The Sulfo-NHS-LC-biotin and the streptavidin beads were from Pierce.
β-Actin antibody (1:10,000) was purchased from Sigma. The APP C-terminal
antibody (A8717; 1:1000) was either purchased from Sigma or received as a
generous gift from Dr. Sam Gandy. The Ubiquilin 1 antibody (1:160) was
purchased from Zymed. The PARP antibody (#9542; 1:1000) was from Cell
Signaling Technology.The HRP-conjugated secondary antibodies (anti-mouse
and anti-rabbit) (1:10,000) were purchased from GE.
Plasmids: The plasmids APP-Gal4, Gal4-UAS-luciferase (encoding firefly
luciferase) were kindly provided from Dr. Thomas Südof, and are described
elsewhere [16]. Ubiquilin 1 over-expression plasmid, which was constructed
from pCMV vector, was kindly provided by Dr. Mervyn J. Monteiro.
Cell culture: SH-SY5Y and HEK-293 cells were purchased from the
American Type Culture Collection (ATCC). These cell lines were cultured in
Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal bovine serum, 2 mM L-glutamine, 100 units/ml penicillin, and 100 μg/ml streptomycin. SH-SY5Y cells that stably express APP-Gal4 and the Gal4-UAS- luciferase reporter construct (named as “SY5Y-APP-Gal4 cells“) were described else where[182]. These cells were maintained with media containing 200 μg/ml
G418. SY5Y-APP-Gal4 cells were transfected with non-silencing shRNA construct (not targeting any known genes or “control”) or shUbqln1 construct
118 (targeting Ubiquilin 1 mRNA or “shUBQLN1”). The multiple clonal cells that contain puromycin resistant constructs were selected and continued to grow in
1ug/ml puromycin and 200ug/ml G418 containing media. When cells reach 15 splitting times, they were replaced with fresh batches.
RNAi and stable cell line selection: Plasmid-based shRNA constructs were purchased from Open-Biosystems, Inc (Birmingham, AL). These constructs are part of the human retroviral shRNA library housed at the Drexel University
RNAi Resource Center. SY5Y-APP-Gal4 cells that stably express shRNA constructs of nonsilencing shRNA construct (Open Biosystems catalong #: RHS
1707) and Ubiquilin 1 (Open Biosystems catalog #: v2HS_254856) were described else where [182]. They are described as “SY5Y-APP-Gal4- shUbqln1”( or simply named as “shUbqln1” in certain context) or “SY5Y-APP-
Gal4-Control”(or simply named as “Control in certain context) To construct these stably cell lines, in brief, shRNA constructs were transfected using Arrest-In transfection reagent (Open Biosystems, Inc.), following by selection of stably expressing shRNA clones by adding 2 μg/ml puromycin 24 hours posttransfection. Populations of resistant clones were five to seven days posttransfection. Clones that achieved more than 50% endogenous protein knockdown were grown up and frozen down in liquid nitrogen. These stably cell lines were maintained with media containing 1ug/ml puromycin and 200ug/ml
G418. Ubiquilin1 protein knock-down level was confirmed for every series of experiments. Cells were replaced with fresh batches after 15 splitting times.
Luciferase assay: Luciferase assay has been described previously else
119 where [182]. Briefly the firefly luciferase assays were carried out in the 96 well plate formats. 7,500 cells were seeded into each well. After treatments, conditioned media was aspirated and discarded. 100 μl Glo Lysis Buffer (GLB,
Promega) was added to lyse the cells. For each sample, 30 μl lysate was utilized for firefly luciferase reading, and another 30ul lysate was utilized for SYBR Green reading (Invitrogen). The individual luciferase signal was further divided by by the
SYBR Green reading for the same well for normalization.
Western blotting analysis: Western blotting analysis was carried out by the method described previously [124, 182]. Briefly cells were lysed in the radioimmunoprecipitation (RIPA) cell lysis buffer (50 mM Tris-HCL pH 7.4, 150 mM NaCl, 1 mM EDTA, 1% NP-40, 1mM PMSF and 1 μg/ml aprotinin, 1 μg/ml leupeptin, and 1 μg/ml pepstatin). After centrifugation and protein concentration measurement, equal amount of protein was applied to electrophoresis followed by membrane transfer, antibody incubation and signal development.
APP synthesis rate analysis: APP synthesis rate analysis has been described elsewhere previously [124]. SY5Y-APP-Gal4 cells stably expressing control and Ubiquilin 1 shRNAs were split into 6 well plates and reached 90% confluence. Then cells were pretreated in methionine/cysteine – free (starve) medium for 30 minutes. Then cells were treated with starve medium supplemented with 100uCi/ml [35S] methionine/cysteine for 20 minutes. The cells were then washed and lysed with RIPA buffer, and followed by immuniprecipitation with APP-CTF antibody.
120 Cycloheximide degradation time course: This method has been described elsewhere previously [124]. SY5Y-APP-Gal4 cells stably expressing control and Ubiquilin 1 shRNAs were split into 6 well plates and reached 90% confluence. Then cells were treated with 30ug/ml cycloheximide for 0, 0.5, 0.75,
1, 2, and 3 hours. Cells were washed and lysed with RIPA lysis buffer and applied to Western blotting analysis. Membrane was blotted with APP-CTF and
β-actin specific antibodies. Mature and immature APP at each time point was normalized to the APP level at the time of 0h.
Biotinylation of cell surface proteins: Biotinylation of cell surface proteins was following the manufacture’s protocol and has been described previously [124]. SY5Y-APP-Gal4 cells that stably express control and Ubiquilin 1 constructs were grown on the 60CM plates. Cells were washed 3 times and preincubated with 20 min in cold Mg2+/Ca2+ containing PBS. Then cells were incubated with 0.5mg/ml Sulfo-NHS-LC- Biotin (Pierce) for 30 minutes with gentle rocking at room temperature. Excess biotin was quenched with 0.1M glycine for
20 minutes. Cells were then washed and lysed in RIPA lysis buffer and immunoprecipitated with streptavidin beads (Pierce) overnight at 4oC. Each sample was boiled at 95 oC for 5 min then applied to Western blotting analysis.
Membrane was incubated with APP-CTF antibody.
MTT assay: The MTT assay was carried out using the MTT cell proliferation assay kit (ATCC BioproductsTM) according to the manufacturer’s instructions.
Briefly, 5,000 cells were plated per well on the 96 well plates. Cells were challenged with 500μM H2O2 for 1 hour and 10ul MTT reagent was added to the
121 cells. After 3 hours of incubation at 37oC, 100ul detergent reagent was added to the cells. After the other 2 hours of incubation in darkness at 37oC, absorbance was read at 570nm using a spectrophotometer (Multiskan spectrum, Thermo
Labsystems).
Caspase-3 activity assay: The Caspase-3 activity assay was following the manufacturer’s instructions (Molecular Probes®). The EnzChek® caspase-3 assay kit #2 was utilized. In brief, 2×106 cells were seeded on a 6 well plate. After inducing apoptosis using 500μM H2O2 for one hour, cells were washed with phosphate-buffered saline (PBS) and lyzed using the lysis buffer provided by the manufacture, followed by centrifugation. Supernatant was transferred to a new white color 96-well plate and added with working solution containing caspase-3 substrate. After covering the plate in dark for 30 minutes, fluorescence was read with the excitation/emission of ~496/520nm on a microplate fluorometer
(Fluoscan Ascent FL, Thermo Labsystems).
Aβ measurement: Aβ measurement was following the method described previously [124]. In brief, Aβ40 and Aβ42 levels (pg/ml) were quantified using sandwich enzyme-linked immunosorbent (ELISA) assay. Each experiment was carried out at least in triplicate.
Statistical Analysis: Values in the text and figures are demonstrated as means ± standard errors from at least three independent experiments. Equal variance two-sample student’s t-test was utilized to compare two groups followed by Bonferroni’s test if multiple comparisons were examined within a single experiment.
122
Results
Ubiquilin 1 protein level is stably and robustly knocked down. shRNA constructs used here are plasmid-based and contain puromycin resistance marker. Cells stably expressing shRNA control or Ubiquilin 1 were generated by selecting for puromycin resistant clones. SY5Y-APP-Gal4 cells that stably express firefly luciferase were established by co-transfecting the APP-Gal4 and
Gal4-UAS luciferase reporter components developed by Cao and Südhof [16] into SH-SY5Y, human neuroblastoma, cell line[182]. SY5Y-APP-Gal4 cells were transfected with non-silencing construct (not targeting any known genes or
“control”) or Ubiquilin 1 construct (targeting Ubiquilin 1 mRNA or “shUBQLN1”).
The multiple clonal cells that contain puromycin resistant constructs were selected and continued to grow in 1ug/ml puromycin and 200ug/ml G418 containing media. We have five different shRNA constructs that target Ubiquilin 1 as have been described previously. The construct of v2HS_254856 gave the most dramatic knock-down effect and has been routinely grown and frozen down in liquid nitrogen. Ubiquilin1 protein was robustly knocked down after re-thawing from liquid nitrogen (Figure1). Ubiquilin 1 protein knockdown was confirmed for every series of experiments. The knock-down constructs achieved using this method can maintain their knockdown effect for at least 3 months.
Ubiquilin 1 does not affect APP synthesis and degradation. Ubiquilin 1 knockdown decreases APP steady state levels post-transcriptionally level in SH-
SY5Y cells[182]. To determine if the result is on APP synthesis or degradation,
123 we compared APP synthesis and degradation in cells expressing control or
Ubiquilin 1 shRNA. Ubiquilin 1 knockdown results in around 10% increase in
synthesized APP than the control, however, there was no statistical significance
between them (P>0.05) (Figure 2A, 2B). To study if Ubiquilin 1 can alter APP
degradation rate, cycloheximide time course experiment was carried out. Control
and shUbqln1 cells were incubated with cycloheximide for 0, 0.5, 0.75, 1, 2 and 3
hours. Western blot analysis of APP revealed that mature and immature APP
levels decreased at the same rate (Figure 2C, 2D). However at each time point,
mature to immature APP is lower when comparing shUbqln1 to control cells
(Figure 2E), which is consistent with our previous results in these cell lines[182].
Taken together, Ubiquilin 1 does not alter APP half life in SH-SY5Y cells, but
alters mature to immature APP ratio.
Ubiquilin 1 knockdown decreases cell surface APP. We have previously reported that Ubiquilin 1 alters APP secretion differently between SH-SY5Y cells and HEK293 cells. In all these cell lines Ubiquilin 1 knock-down decreases APP
steady state holoprotein levels. However, Ubiquilin 1 knock-down decreases
sAPPα in SH-SY5Y cells and increases sAPPα in HEK293 cells. Cell surface
biotinylation was employed to study what effect of Ubiquilin 1 knockdown on APP
trafficking at the cell surface in SH-SY5Y cells. Ubiquilin 1 knockdown
significantly decreased cell surface APP by around 15% (P<0.05) (Figure 3).
Ubiquilin 1 effects on APP metabolism are dependent on proteasome
system. It was suggested that Ubiquilin 1 is an adaptor protein which delivers
ubiquitinated proteins to the proteasome for degradation [173, 174, 176]. To
124 determine if Ubiquilin 1 mediated effects on APP metabolism are dependent on the proteasome, we inhibited proteasome utilizing MG132. Control and shUbqln1
SY5Y-APP-Gal4 cells were treated with 10μM MG132 for 1 hour. Cell lysates were collected and applied to luciferase or Western blot analysis. Luciferase assay data revealed that MG132 treatment decreased AICD-mediated lucifease
(P<0.01) on the control cells. However there was no significant difference in
AICD-mediated luciferase activity in Ubiquilin 1 knockdown cells between the
MG132 treatment and non-MG132 treatment (P>0.05) (Figure 4A). Using
Western blot analysis to confirm this finding, when MG132 was applied to control cells, mature APP levels decreased (P<0.05) and immature APP level also decreased (P>0.05). However, MG132 treatment of Ubiquilin 1 knockdown cells did not alter levels of mature or immature APP levels (P>0.05) In addition,
(Figure 4B, 4C, and 4D). Taken together, it is suggested that Ubiquilin 1 effect on
APP processing is dependent on the proteasome system. In addition we
observed that the chances in C83-Gal4 and AICD-Gal4 upon MG132 treatment.
Ubiquilin 1 alters cells viability and caspase-3 activity Because Ubiquilin
1 is indicated to be involved in delivery proteins to the proteasome for
degradation and prevent accumulation of misfolded proteins, down-regulating
Ubiquilin 1 may decrease cell viability and lead cells go through apoptosis. To test this hypothesis, control and shUbqln1 stably knockdown SY5Y-APP-Gal4 cells were challenged with 500μM H2O2 for 1 hour. Cell viability and caspase-3
activity was then measured. Compared with control cells, Ubiquilin 1 knockdown
cells significantly decreased cell viability (P<0.05) and elevated the caspase-3
125 activity (P<0.05) under the H2O2 stress (Figure 5A, 5B). Full length PARP is
cleaved upon caspase-3 activation. Interestingly Ubiquilin 1 over-expression
resulted in decreased levels of full length and cleaved PARP (Figure 5C).
Ubiquilin 1 degradation requires proteasome and lysosome It has not
been investigated how Ubiquilin 1 is degraded. To determine how Ubiquilin 1 is
degraded, we utilized MG132 and chloroquine to block the proteasome and
lysosome degradation systems, respectively. Naïve SH-SY5Y cells were challenged with 10μM MG132 or 20μM chloroquine for 1h, and cell lysates were prepared and utilized for Western blotting analysis. MG132 and chloroquine dramatically increased Ubiquilin 1 protein levels respectively in naïve SH-SY5Y cells (Figure 6A). MG132 significantly increased Ubiquilin 1 protein levels about 5 fold in SH-SY5Y cells (p<0.05). Chloroquine increased Ubiquilin 1 protein levels about 2 fold in SH-SY5Y cells, though it did not reach the statistical significance
(Figure 6B).
Ubiquilin 1 over-expression decreases Aβ levels. Decreasing Aβ level is a therapeutic strategy for AD treatment, and proteins that could decrease Aβ level will be considered good therapeutic target. It has not been investigated if
Ubiquilin 1 can decrease Aβ levels. To test the hypothesis that Ubiquilin 1 is an
AD therapeutic target, naïve HEK-293 cells were transiently transfected with
Ubiquilin 1 for 48h. Conditioned media was prepared and utilized for ELISA.
Overexpression of Ubiquilin 1 decreased secreted Aβ40 level about 40%
(p<0.05) and Aβ42 level up to about 60% (p=0.09) (Figure 7A). The ratio of Aβ42
126 to Aβ40 decreased up to 40% though it did not reach the statistical significance
level (Figure 7B).
γ-Secretase inhibition increases Ubiquilin 1 protein levels Since it
indicated that Ubiquilin 1 could be a potential AD therapeutic target, it is
important to identify regulators and pathways that modulate Ubiquilin 1 activities.
We and other groups have shown that Ubiquilin 1 modulates PS1 proteolysis and
therefore γ-secretase activity, therefore, we determined to investigate if modulation of γ-secretase activity regulates Ubiquilin 1. L685,458, a γ-secretase
inhibitor, was utilized to test its ability to modulate Ubiquilin 1. L685,458
increased Ubiquilin 1 steady state protein levels about 5 fold (p<0.05) in naïve
SH-SY5Y cells and about 4 fold in naïve HEK-293 cells (p<0.01) after
normalizing to gel loading (Figure 8A, 8B, 8C, and 8D).
127
Discussion
Ubiquilin 1 modulates cell surface APP levels It has been indicated that
Ubiquilin 1 modulates APP metabolism on a post-transcriptional level[124, 182].
To gain insight into the mechanism of the effects, we studied Ubiquilin 1 influence on APP half life using APP synthesis and degradation rate analysis in our previously reported SY5Y-APP-Gal4 stable cell line. Our results showed that
Ubiquilin 1 does not alter APP half life, the same conclusion as what was observed in HEK293 cells [124]. A consistent finding was observed that mature/immature APP ratio from Ubiquilin 1 knock-down cells was lower than control cells with cycloheximide treatment. This was also consistent with what has been observed previously. Ubiquilin 1 knock-down has been shown to increase sAPPα and cell surface APP in HEK293 cells [124]. However we found previously that Ubiquilin 1 knock-down decreases sAPPα in SH-SY5Y cells. Here we also found Ubiquilin 1 knock-down decreases cell surface APP. The trend of sAPPα changes appears to be the same as that of cell surface APP. Taken together Ubiquilin 1 alters APP metabolism in a cell type dependent manner.
Ubiquilin 1 does not alter APP half life in SH-SY5Y cells but it alters cell surface
APP, as well as the mature to immature APP ratio. Knock-down of Ubiquilin 1 in
SH-SY5Y cells decreases APP holoprotein levels and cell surface APP, as well as decreases mature to immature APP ratio.
Ubiquilin 1 modulation on APP is dependent on the proteasome
Ubiquilin 1 has been shown to specifically deliver polyubiquitinated proteins to
128 the proteasome machinery for degradation. Interestingly some evidences
suggested that Ubiquilin 1 functions do not require proteasome. In other words,
Ubiquilin 1 functions could be dependent or independent on the proteasome system. The role of proteasome in Ubiquilin 1 functional regulation of APP metabolism has not been investigated. We found that Ubiquilin 1 effects on APP metabolism in SH-SY5Y cells are dependent on the proteasome system. MG132, a proteasome inhibitor, was applied to the SY5Y-APP-Gal4 cells that stably express control and Ubiquilin 1 shRNAs. MG132 decreased AICD-mediated luciferase activity, as well as mature and immature APP levels in control cells, but not in Ubiquilin 1 knock-down cells. We conclude that Ubiquilin 1 effects on APP metabolism are dependent on proteasome.
Ubiquilin 1 alters cells viability and caspase-3 activity Evidence has shown that Ubiquilin 1 is involved in the endoplasmic reticulum (ER) and UPS
(Ubiquitin-proteasome system), which are important components of PQC (protein quality control) system [156, 165]. ER is the other eukaryotic organelle for protein folding and degradation. UPS is essential for many cellular processes, including the cell cycle, the regulation of gene expression, and responses to oxidative
stress. A number of lines of experiments have shown that Ubiquilin 1 can alter
intracellular toxicity and apoptosis [175, 187]. Ubiquilin 1 has also been
implicated in the pathophysiology of cancer [167, 192].
Apoptosis, or programmed cell death, plays a critical role in development as
well as in degenerative diseases, such as AD and cancer [191]. It is indicated
Ubiquilin 1 is also involved in apoptosis from the report that Ubiquilin 1 could
129 alleviate the hypoxia induced apoptosis in SH-SY5Y cells. Recently members of
the interleukin-1β-converting enzyme (ICE)/Ced-3 proteases (caspases) family of
protease have been found to be pivotal mediators in apoptosis. It cleaves
specifically Asp-Glu-Val-Asp (DEVD) amino acid sequence containing proteins,
including Poly (ADP-ribose) polymerase (PARP), which is essentially involved in
DNA repair in response to environmental and intracellular stress. There are no
reports whether Ubiquilin 1 could alter caspase-3 activity and PARP protein
levels.
To gain insight into the mechanism of how Ubiquilin 1 modulate cell viability,
we found that Ubiquilin 1 knock-down decreased cell viability under H2O2
treatment in SH-SY5Y cells. Ubiquilin 1 knock-down elevated caspase-3 activity
under H2O2 treatment. Ubiquilin 1 over-expression decreased full length PARP
and cleaved PARP levels. Taken together, Ubiquilin 1 alters caspase-3 activity
and modulates PARP metabolism.
Uiquilin 1 degradation involves both proteasome and lysosome
Intracellular and extracellular proteins are degraded through two major systems:
the UPS and lysosome. UPS primarily degrades cytosolic regulatory proteins and
misfolded proteins. Lysosome primarily degrades larger misfolded proteins or
organelle through hydrolytic reactions in its acidic environment. Understanding β-, and γ-secretases activity and degradation pathway is important to understating
APP and Aβ metabolisms. BACE is degraded through UPS and lysosome. All the four essential γ-secretase components (PS1, PEN2, NCT and APH1) have been shown to undergo UPS degradation[193-196]. BACE and NCT can also undergo
130 lysosome degradation[195, 197]. Uiquilin 1 degradation pathway has not been investigated. Here we found that Ubiquilin 1 undergo UPS and lysosome degradation pathway in SH-SY5Y cells.
γ-Secretase inhibition increases Ubiquilin 1 protein levels Our data showed the first time that over-expression of Ubiquilin 1 decreases Aβ level, as well as the ratio of Aβ42/Aβ40, which also suggested that Ubiquilin 1 can be a therapeutic target for the intervention of AD. We also found that the γ-secretase inhibition by L685,458 could dramatically elevate Ubiquilin 1 steady state protein levels in SH-SY5Y and HEK293 cells. This finding strengthens the current therapeutic strategy to utilize γ-secretase inhibitors as intervention for AD.
Moreover, it suggests that Ubiquilin 1 levels are regulated by the γ-secretase activity, a process that Ubiquilin 1 can also regulate by controlling PS1 endoproteolysis. These results suggest that a regulatory circuit exists to coordinate the levels and activities of γ-secretase and Ubiquilin 1.
In summary we characterized the role of Ubiquilin 1 in APP metabolism and its involvement with the proteasome system. First we found that Ubiquilin 1 alters
APP trafficking without affecting APP half-life. Next, Ubiquilin 1 interacts with proteasome system. Uiquilin 1 effects on APP metabolism are suggested to be dependent on the proteasome. Uiquilin 1 alters cell viability, as well as the caspase-3 activity and PARP levels. Moreover, Ubiquilin 1 undergo ubiquitin- proteasome system and lysosome. Finally, γ-secretase inhibition dramatically increases Ubiquilin 1 protein level, which suggests that a regulatory circuit exists to coordinate the levels and activities of γ-secretase and Ubiquilin 1.
131
Figures
Figure 3-1: Uiquilin 1 protein level is robustly knocked down. SY5Y-APP-
Gal4 cells that express luciferase expressing constructs were transfected with
scrambled shRNA sequence (not targeting any known genes; or “control”) or shUbqln1 sequence (targeting Ubiquilin 1 mRNA; or “shUBQLN1”) and selected with 2ug/ml puromycin for 7-10 days. This figure demonstrates that Ubiquilin 1
protein level was still dramatically knocked down after freezing and thawing from
the liquid nitrogen. β-Actin serves as a loading control.
132 A B
C
D E
Figure 3-2: Ubiquilin 1 knock-down does not change APP half life, but
changes mature and immature ratio. A. Ubiquilin 1 knockdown does not
alter APP synthesis rate. SY5Y-APP-Gal4 cells stably expressing control and
Ubiquilin 1 shRNAs were pulsed for 30 minutes with [35S]-Met. Total protein
lysates were immunoprecipitates with the APP-CTF antibody and exposed for
phosphorimaging screen for quantification. A protein that is about 35 kDa size
and non-specific to APP antibody served as the loading control. B. Normalized
133 densitometry for figure A. There is no statistical difference between control and
shUbqln1 for the newly synthesized APP (P>0.05). C. Ubiquilin 1 knockdown does not change APP degradation rate. SY5Y-APP-Gal4 cells stably expressing control and Ubiquilin 1 shRNAs were treated with 30μM cycloheximide for 0, 0.5, 0.75, 1, 2, and 3 hours. Cells lysates were collected at each time point and utilized for Western blotting analysis. D. Normalized
densitometry for figure C. Ubiquilin 1 knock down does not alter APP
degradation rate. E. Calculated mature APP to immature APP ratio from
densitometry data of figure C. Mature to immature APP ratio from Control was
higher than shUbqln1 at each time point during the cycloheximide treatment.
134 A B
Figure 3-3: Ubiquilin 1 knockdown decreased cell surface APP. A. SY5Y-
APP-Gal4 cells stably expressing control and Ubiquilin 1 shRNAs were
biotinylated with Sulfo-NHS-LC-biotin for 30 minutes at 4OC, immununoprecipated with streptavidin-agarose beads and applied to Western blotting analysis. Ubiquilin 1 knock-down decreased cell surface APP levels. B.
Normalized densitometry. Ubiquilin 1 knock-down significantly decreased cell surface APP levels (P<0.05).
135 A B
C D
[198] E F
Figure 3-4: Ubiquilin 1 effect on APP metabolism modulation is dependent on proteasome system. A. 1h treatment of 10μM MG132 significantly
136 decreased luciferase activity in SY5Y-APP-Gal4 cells stably transfected with
control shRNA (p<0.01). Mock contained the same amount of DMSO as MG132
sample. B. 1h treatment of 10μM MG132 did not change luciferase activity in
SY5Y-APP-Gal4 cells stably transfected with Ubiquilin 1 shRNA (p>0.05). C.
MG132 decreased mature and immature APP steady state protein levels in
SY5Y-APP-Gal4 cells stably expressing control shRNAs. D. Normalized densitometry figure C. MG132 significantly decreased mature APP protein levels, but did not significantly alter immature APP protein levels. E. MG132 did not decrease mature and immature APP steady state protein levels in SY5Y-APP-
Gal4 cells stably expressing Ubiquilin 1 shRNA. F. Normalized densitometry for
figure E. MG132 did not change mature and immature APP protein levels
(p>0.05).
137 A B
C
Figure 3-5: Down-regulation of Ubiquilin 1 decreases cell viability, as well
as elevates caspase-3 activity. A. Ubiquilin 1 knock-down significantly
decreased cell viability in SY5Y-APP-Gal4 cells utilizing the MTT assay (p<0.05).
SY5Y-APP-Gal4 cells stably expressing control and Ubiquilin 1 shRNAs were
incubated with 500μM H2O2 for 1 hour, and then subjected to MTT assay to
measure cell viability. B. Ubiquilin 1 knock-down significantly elevates caspase-3
activity. SY5Y-APP-Gal4 cells stably expressing control and Ubiquilin 1 shRNAs
were incubated with 500μM H2O2 for 1 hour, and then utilized in a caspase-3
activity assay (p<0.05). The caspase-3 activity of shUbqln1 cells was compared
to control cells. C. Ubiquilin 1 over-expression decreased PARP protein levels.
138 SY5Y-APP-Gal4 cells were transiently transfected with Ubiquilin 1 over- expression plasmid. Cell lysates were prepared 2 days post transfection and applied to Western blotting analysis. The first antibody was anti PARP which can detect both the full length (119 kDa) and cleaved PARP protein (89 kDa).
139 A B
Figure 3-6: Ubiquilin 1 degradation requires proteasome and lysosome. A.
MG132 and chloroquine dramatically increase Ubiquilin 1 protein levels respectively in naïve SH-SY5Y cells. Naïve SH-SY5Y cells were treated with
10μM MG132 or 20μM chloroquine for 1 hour, and then cell lysates were collected and applied to Western blotting analysis. β-actin was utilized as the loading control. B. Normalized densitometry for figure A. MG132 significantly increased Ubiquilin 1 protein levels about 5 fold in SH-SY5Y cells (p<0.05).
Chloroquine increased Ubiquilin 1 protein levels about 2 fold in SH-SY5Y cells, though it did not reach the statistical significance.
140 A B
Figure 3-7: Ubiquilin 1 decreases Aβ levels, as well as Aβ42/Aβ40 ratio.
Naïve HEK293 cells were transiently with Ubiquilin 1 over-expression plasmids.
Conditioned media was prepared 48 h post trasnsfection and utilized for Aβ
ELISA assay. Ubiquilin 1 over-expression significantly decreases Aβ40 (P<0.05)
(Figure A). Ubiquilin 1 over-expression decreases Aβ42 levels, as well as
Aβ42/Aβ40 ratio, though statistically significance was not reached (Figure A & B).
141
A B
C D
Figure 3-8: γ-secretase inhibition increases Ubiquilin 1 protein levels. A.
L685,458 dramatically increased Ubiquilin 1 protein levels in SH-SY5Y cells.
Naïve SH-SY5Y cells were treated with 5μM L685,458 for 1 hour. Cell lysates were prepared and applied to Western blotting analysis. β-Actin served as the loading control. B. Normalized densitometry for figure A. L685,458 significantly increased Ubiquilin 1 protein levels about 5 fold in SH-SY5Y cells (p<0.05). C.
L685,458 dramatically increases Ubiquilin 1 protein levels in HEK-293 cells. D.
Similar to A, naïve HEK293 cells were treated with 5μM L685,458 for 1 hour. Cell lysates were prepared and applied to Western blotting analysis. D. Normalized
142 densitometry for figure C. L685,458 significantly increase Ubiquilin 1 protein levels about 4 fold in naïve HEK293 cells (p<0.01).
143
CHAPTER 4
Discussion and future directions
In the first chapter, I introduced the background and status of Alzheimer’s disease (AD) research. Evidence from genetics, molecular biology and biochemistry studies indicates that APP metabolism is a central event in AD; however, the underlying mechanism is not completely understood. We aim to identify novel APP metabolism regulators and therefore identify therapeutic targets for AD. In the second chapter, I described the establishment and validation of an AICD-mediated assay to identify novel APP metabolism regulators in my laboratory. Next in the third chapter I explored our screening assay on one of the chromosomal regions linked to AD. A number of putative
APP metabolism regulators have been identified and their functional properties are undergoing further characterization. Here I will further discuss the results of my screening assay, as well as one of the “hits” I have further functionally characterized.
Utilization of AICD mediated assay to identify novel APP metabolism regulators APP metabolism is considered a central event of AD and the traditional way to investigate APP metabolism is to measure Aβ levels.
Compared to the approach of Aβ measurement, our assay has several advantages. The first one is that the cost of our assay is low. The readout of our
144 AICD-mediated assay is luciferase, which is usually measured on a 96 well plate
and the cost is at least 5 fold lower than the Aβ measurement using Enzyme-
Linked ImmunoSorbent Assay (ELISA). The second advantage is the speed.
Similar to the first reason, luciferase assay takes less than 1 hour after the lysate preparation. However it takes more than 5 hours measuring Aβ levels utilizing
ELISA after collecting conditioned media. The third advantage is reliability. To
evaluate the quality of our assay, the Z parameter was utilized which takes into
account the data range and variation. All the known APP metabolism regulators
revealed a greater than 0.5 value of Z factor. This suggests that our assay is
reliable and robust to detect APP metabolism regulators. The last but not the
least advantage is the precision. Since lucferase is utilized as the alternative
representative of the protein activity of AICD-Gal4, we calculated their correlation
and regression relationship using known APP metabolism regulators. The data
from Figure 2-8 demonstrates that these two factors represent each other, and
luciferase represents AICD-Gal4 protein level precisely. Taken together I am
confident that our assay is robust and reliable to identify novel APP metabolism
regulators.
I utilized the AICD-based screening assay to test genes on chromosome
9q22 which is a region linked to AD. There are 112 known genes in this region and the Drexel University RNAi resource center maintains shRNA constructs that target 83 genes. In the screening approach, the negative controls are non-coding shRNA (its sequence does not match any known mRNA sequences in human) and eGFP shRNA (enhanced green fluorescence protein; its protein is not
145 expressed in the assay cells). The positive controls are APP and ADAM10
shRNAs that are confirmed to alter AICD-mediated luciferase activities. For the
data analysis, we used two sample, two-tail, t-test followed by sequential
Bonferroni test to correct comparisons from multiple groups. To further lower the
potential of false positive errors, we confirmed each gene as a “hit” only if we
observed the effects in multiple constructs or the same construct in multiple
independent experiments (for single construct genes). Using these rigorous
criteria, we identified 11 “hits”. These “hits” require further functional characterization because of the “off-target” effect of shRNAs. Over-expression,
Western blotting analysis and other experiments will be carried out for further characterization.
Characterization of Ubiquilin-1 as an APP metabolism regulator
Ubiquilin 1 is one of the “hits” that modulate APP metabolism. It belongs to the
Ubiquilin family which has 3 other Ubiquilin 1 homologues: Ubiquilin 2, Ubiquilin 3, and Ubiquilin 4. They share similar functional structures and all contain an UBA
(ubiquitin-asociated domain) on the N-terminus and an UBL (ubiquitin-like domain) on the C-terminus. Ubiquilin family proteins have been shown to interact
with a number of proteins that carry out various functions, including ER- associated quality control and degradation (ERQD or ERAD) [155, 169, 170],
DNA repair[199], spindle pole duplication[200-203] , cell cycle arrest[201], cell adhesion[204] and apoptosis[175]. Ubiquilin family proteins interact with more than 10 proteins. Some proteins are dependent on the proteasome degradation
[144, 174, 175]; some seem to be independent of the proteasome
146 degradation[176], and others are unidentified whether they need proteasome or
not[172]-[177]. Ubiquilin family proteins have also been implicated in the
pathophysiology and pathogenesis of Alzheimer’s disease[125, 127, 137, 181,
182], PolyQ diseases[187] [186]., and cancer[188] [189] [167]. Arising from all
these puzzling and multiple-aspect facts, the overall Ubiquilin function seems to
be protein quality control (PQC) and the underlying mechanism is still not fully
understood. This is discussed in detail in the third chapter.
Figure 4-1 Route of protein trafficking. Molecular Biology of Cell, 4th edition,
Page 665, Garland Science.
147
Figure 4-2 APP synthesis and degradation.
I focused on the study of Ubiquilin 1 mediated APP metabolism regulation regardless of the other proteins that Ubiquilin1 interact with. Figure 4-1 shows the general protein trafficking. Figure 2 illustrates the APP synthesis and degradation pathway, which include the secretory pathway and the endocytic pathway. APP is a type I transmembrane glycoprotein and it is present on almost every intracellular organelle and plasma membrane (or cell membrane). It is suggested to function in neuroprotection, synaptic transmission, signal transduction, and axonal transport [101, 102]. APP maturation and trafficking was shown in Figure
4-2. Upon being transcribed and translated, APP protein undergoes N-
148 glycosylation in the ER and cis-Golgi. Then it undergoes O-glycosylation in medial- , trans-Golgi and trans-Golgi network (TGN) to become mature. Mature
APP is transported to mitochondria, peroxisomes and other organelles, as well as
plasma membrane by secretory vesicles. If the plasma membrane APP is not
cleaved by α-, or β- secretases, it will be reinternalized within clathrin-coated
vesicles to an endosomal/lysosomal degradation pathway.
During the APP cleavage pathway, the initial cleavage by either α- or β-
secretase is the rate-limiting step. β-, and γ- secretase are present on almost
every intracellular organelle and plasma membrane. BACE has maximal activity
at acidic pH, and its activity is highest in the acidic subcellular compartments,
including the Golgi apparatus and endosomes, which are the places where most
Aβ is generated[198]. α-secretase has the highly cleavage activity on the plasma
membrane. Therefore, Aβ is generated intracellularly and extracellularly.
Molecules that modulate APP trafficking in these organelles and plasma
membrane may change the Aβ production and modulate AD risk. My data show
that Ubiquilin 1 alters APP maturation and trafficking to the cell surface, as well
as presenilin 1 endoproteolysis. The underlying mechanism is still not known.
Gandy et al showed the familial AD mutant PS1 shifted the holoAPP and APP
CTFs toward TGN in vivo[198]. It is not sure whether, and possibly to what level,
the altered PS1 endoproteolysis affect APP maturation and trafficking. This is a
line of investigation that will be further studied whether Ubiquilin 1 alters APP
trafficking to the TGN or endosome. The second future study is to investigate
whether proteasome blockage affects Ubiquilin 1 effects on PS1 endoproteolysis.
149 The third and also important future research question is to study the underlying mechanism of how Ubiquilin 1 alters APP maturation and trafficking. This line of investigation could be carried out by inhibiting or stimulating protein secretory and/or endocytic pathway when Ubiquilin 1 is up-, or down-regulated. The fourth line of future research is to construct an Ubiquilin 1 interaction network and study the mechanism of how Ubiquilin 1 interacts with so many proteins with various functions. It will be also interesting to investigate whether alteration of other
Ubiquilin 1 interacting proteins may change APP metabolism. Last but not least is the hypothesis of using Ubiquilin family proteins as therapeutic targets for AD, and possibly also for other neurodegenerative disorders and cancers. It will be crucial to investigate its global effects on other interacting proteins.
Functional characterization of other APP metabolism regulators 11 genes have been identified from chromosome 9q22 using our AICD-mediated luciferase assay. Table 4-1 shows their name and key functions. Most recently four of them (CTSL, NTRK2, SHC3 and UBQLN1) have been implicated in AD risk or APP metabolism. Here I show, for the first time, that the other 7 genes could be involved in APP metabolism.
Down-regulation of five genes decreased AICD-mediated luciferase and therefore they are considered positive APP metabolism regulators: CTSL
(Cathepsin L), CTSL2 (Cathepsin L2), FRMD3 (FERM domain containing 3),
NTRK2 (Neurotrophic tyrosine kinase, receptor, type 2), and UBQLN1 (Ubiquilin
1). Here I will briefly describe their essential functions and emphasize their
involvement in APP metabolism and disorders.
150 CTSL (Cathepsin L, or CTSL1) is a papain-type cysteine proteinase whose
activity is present in the lysosome and clathrin-coated vesicles[205, 206], and is
capable of initiating the processing of APP or its fragments[206], and possibly involved in the processing of APP to Aβ [207].
Differently from CTSL, the CTSL2 gene encodes the cysteine protease cathepsin V (Cat V), also called L2. It is involved in antigen processing and antigen presentation by MHC class II molecules [208]. Polymorphisms in CTSL2 gene show association with type 1 diabetes and early-onset myasthenia gravis[208].
FRMD3 (Ferm domain containing protein 3) protein is a member of the protein 4.1 superfamily, which are characterized by the presence of a conserved
FERM (Four.1 protein, Ezrin, Radixin, Moesin) domain and a spectrin/actin
binding domain (SABD). These proteins function to link cell surface glycoproteins
to the actin cytoskeleton [209]. Recently FRMD3 is implicated in the origin and
progression of lung cancer[210].
NTRK2 (Neurotrophic tyrosine receptor kinase, type 2), also named as
TRKB (Tyrosine receptor kinase, type B), belongs to the NTRK or TRK protein family, which includes NTRK1/TRKA, NTRK2/TRKB, and NTRK3/TRKC. The
family proteins NTRK1, NTRK2 and NTRK3 bind three kinds of neurotrophins,
NGF (Nerve growth factor), BDNF (Brain-derived neurotrophic factor) and NT-3
(neurotrophin-3) with high affinity, respectively. The ligand and receptor binding
activates a wide range of downstream intracellular cascades, regulating neuronal
development and plasticity, long-term potentiation, and apoptosis [211-213]. Most
151 recently NTRK2 is implicated to be a genetic susceptibility gene contributing to
AD pathology[211].
UBQLN1 (Ubiquilin 1), an UBA (ubiquitin-asociated domain) and an UBL
(ubiquitin-like domain) containing protein, has been indicated to be an adaptor
protein transporting polyubiquitinated proteins toward proteasome for
degradation. A polymorphism in UBQLN1 has been shown to increase AD
risk[137]. Ubiquilin 1 protein belongs to the Ubiquilin protein family which
contains 4 members that are primarily involved in protein degradation and protein
quality control. It functions to modulate protein steady state levels, or maturation,
or half life levels and influences more than 10 proteins including APP, dependent
or independent of the proteasome[124, 127, 143, 173, 175, 182]. Ubiquilin 1
modulates APP maturation, as well as PS1 endoproteolysis[127]. More Ubiquilin
1 details are described in chapter 3.
Down-regulation of six genes identified using our AICD-mediated
luciferase assay increased the luciferase activity, and therefore they are
considered negative APP metabolism regulators: KIF27 ( Kinesin family member
27), NR4A3 (Nuclear receptor subfamily 4, group A, member 3), PPP3R2
(Protein phosphatase 3, regulatory subunit B, beta isoform), SPTLC1 ( Serine
palmitoyltransferase, long chain base subunit 1), SHC3 (Src homology 2 domain-
containing transforming protein C3), and TMOD1 (Tropomodulin 1). Next I will briefly describe their essential functions and emphasize their involvement in APP metabolism and disorders.
152 KIF27 (Kinesin family member 27) is the mammalian homolog of the
Drosophila costal2 (cos2) gene which encodes a kinesin-related protein [214]. In flies, cos2 is involved in the Hedgehog (Hh) signal transduction pathway [215-
217], which is essential in the embryonic patterning and development in the
Drosophila and mammals[218]. The hh gene itself is a segment polarity gene
[219] that encodes the secreted ligand required to activate the Hedgehog signaling pathway. Cos2 protein is bound to microtubules in a complex and is released in a Hh-dependant manner [214, 220] . The complex contains Ser/Thr kinase Fused (Fu), as well as the Suppressor of Fused (Su (fu)) [214, 220].
NR4A3 (Nuclear receptor subfamily 4, group A, member 3) belongs to a
NGFIB-like (nerve growth factor IB-like) subfamily of the NHR (nuclear hormone receptor) family. The subfamily has identified three members including NR4A1
(or NGFIB) , NR4A2 (or NURR1/nucclar receptor related 1), and NR4A3 ( or
NOR1/neuron-derived orphan receptor 1) . These three members share similar structural features and have yet known natural ligands. They are transcriptional factors that function through activation and subsequent induction of the downstream pathways, and are essential in the temporal regulation of genes and implicated in the cell survival, apoptosis and tissues development[221]. NR4A2 and NR4A3 are indicated to be critical tumor suppressors of myeloid leukemogenesis[222].
PPP3R2 (also known as PP2BB2) (Protein phosphatase 3, regulatory subunit B, beta isoform) is one of the two isoforms of PPP3R (Calcineurin B), the regulatory subunit of PPP3 (protein phosphatase 3, or Calcineurin, or PP2B)
153 [223]. PPP3 is a serine/threonine protein phosphatase, and is a heterodimer composed of PPP3C (Calcineurin A, the PPP3 catalytic subunit) and PPP3C,
Calcineurin A) and PPP3R. PPP3R is an EF-hand Ca2+ binding protein and plays critical roles in many calcium-mediated signal transduction pathways.
PPP3R has two isoforms, PPP3R1 (or PP2BB1) and PPP3R2 (or PP2BB2).
Differently from PPP3R1 which is ubiquitously expressed in different tissues,
PPP3R2 seems to be exclusively expressed in testis[223]. I expect that PPP3R2 knockdown should not alter APP metabolism in our neuroblastoma SH-SY5Y cell line. However, AICD-mediated luciferase results showed that PPP3R2 knockdown increased luciferase. Confirmative evidence can be obtained from over-expression experiments and Western blotting analysis.
SPTLC1 (Serine palmitoyltransferase, long chain base subunit 1) is one heterodimer subunit of the mammalian SPT protein. A third subunit, SPTLC3, a homolog of SPTLC1 and SPTLC2 , has also been implicated in the SPT complex[224, 225]. SPT protein complex catalyses the rate-limiting step for the synthesis of sphingolipids, which are essential in cell membrane formation, signal transduction, cholesterol homeostasis, and lipoprotein metabolism. All these functions can modulate atherosclerotic and cardiovascular disease development.
Lipoproteins have also been implicated in AD[226]. Apolipoprotein E (APOE) is the only gene in which a polymorphism has been confirmed to increase AD risk[70, 75].
SHC3 (Src homology 2 domain-containing transforming protein C3; or
ShcC; or N-Shc; or Rai) is one among the more than 100 SH2 domain containing
154 proteins and is one the three mammalian Shc genes including ShcA (or Shc),
ShcB (or Sli/SCK), and ShcC. Its cytoplasmic signal transducers are characterized by the unique PTB-CH1-SH2 modular organization (PTB: phosphotyrosine-binding domains; CH: collagen homology region; SH: src homology 2). Shc3 expression is restricted to neuronal cells and regulates the number of postmitotic sympathetic neurons[227]. It is important in the MAPK and
PI3K signaling pathways, as well as Ret-dependent and -independent survival signals[227]. Recently it has been implicated in the APP metabolism. Shc3, as well as Shc 1 and Fe65 (APBB1) are APP adapter proteins and can bind to and interact with the conserved YENPTY motif in the APP-C terminus. Xie et al recently reported that down-regulation of ShcC led to the reduction of APP-CTFs and Aβ, as well as the reduction of BACE in H4 human neuroglioma cells.
Therefore, they suggested that ShcC could be a therapeutic target against
AD[228].
TMOD1 (Tropomodulin 1) along with 3 tropomodulin proteins (TMOD2, 3, and 4), belongs to the family of tropomodulin. It is a tropomyosin-binding protein and caps the slow-growing (pointed) end of the actin filament, therefore regulates its dynamics. Tropomodulin is essential for determining cell morphology, cell movement, and muscle contraction[229, 230].
155
CTSL: Cathepsin L Potential contribution of the lysosomal compartment in the processing of APP to Aβ CTSL2: Cathepsin L2 A cysteine protease; proteolysis and peptidolysis; cathepsin L activity FRMD3: FERM domain A multifunctional protein essential for maintaining erythrocyte shape containing 3 and membrane mechanical properties. KIF27: kinesin family member Kinesin_motor 27 Receptor for brain-derived neurotrophic factor (BDNF), neurotrophin- NTRK2: Neurotrophic 3 and neurotrophin-4/5; involved in the development and/or tyrosine kinase, receptor, type maintenance of the nervous system. Indicated to be an AD suspitible 2 gene. NR4A3: nuclear receptor Neuron-derived orphan receptor; Mitogen-induced nuclear orphan subfamily 4, group A, member receptor 3 PPP3R2: Protein phosphatase Regulatory subunit of calcineurin, a calcium-dependent, calmodulin 3 (formerly 2B), regulatory stimulated protein phosphatase. calcium ion binding; MAPK signaling subunit B, beta isoform pathway; Apoptosis; Long term memory SPTLC1: serine Catalytic activity: Palmitoyl-CoA + L-serine = CoA + 3-dehydro-D- palmitoyltransferase, long sphinganine + CO(2). Belongs to the class-II pyridoxal-phosphate- chain base subunit 1 dependent aminotransferase family. Signaling adapter that couples activated growth factor receptors to SHC3: Src homology 2 signaling pathway in neurons. Signal transduction pathways of domain-containing neurotrophin-activated Trk receptors in cortical neurons. Modultes transforming protein C3 APP metabolism and Aβ levels. Tropomodulin is highly concentrated at the postsynaptic domain of TMOD1: Tropomodulin 1 human and rat neuromuscular junctions; tropomyosin binding; cytoskeleton; organization and biogenesis; actin binding In vivo protein degradation, modulates accumulation of presenilin UBQLN1: Ubiquilin 1 proteins, and is found in lesions associated with Alzheimer's and Parkinson's disease.
Table 4-1: APP metabolism regulators identified utilizing the AICD-mediated luciferase assay.
Summary Taken together, we have established and validated an AICD-
Gal4 based functional assay in SH-SY5Y cells. Using this assay in combination with RNAi, we have developed a genetic screen to identify regulators of APP metabolism. This screen accurately, robustly, and easily measures changes in
AICD-Gal4 levels. We demonstrate that these AICD-Gal4 levels can be altered by pharmacologic or genetic modulation of genes that directly regulate APP
156 levels, AICD trafficking/signaling, APP maturation, and APP proteolysis. Using
this approach, we identified 11 genes on chromosome 9q22, a region linked with
AD high risk. We further characterized the ubibquilin 1 mediated APP metabolism.
We show that Ubiquilin 1 can regulate AICD-Gal4 levels in SH-SY5Y cells.
Ubiquilin 1 regulates AICD-Gal4 levels by modulating APP levels, the ratio of mature to immature APP, and PS1 endoproteolysis. Interestingly, alteration of
PS1 activity also changes Ubiquilin 1 protein levels. We also demonstrate that
the effect of Ubiquilin 1 on APP metabolism is dependent on proteasome system.
Ubiquilin 1 protein undergoes proteasome and lysosome degradation pathways.
Taken together, the results demonstrate that this genetic screen is capable of
identifying APP metabolism regulators that can modulate the APP proteolytic
processing, APP maturation, APP levels, and AICD trafficking/signaling.
Furthermore, we suggest that there exists a regulatory circuit to coordinate the
levels and activities of γ-secretase and Ubiquilin 1. Characterization of other 10
“hits” through our AICD-mediated assay is required to understand their
involvement in the APP metabolism.
157
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171
VITA
th Can Zhang was born in Heze, Shan Dong Province, China on March 7 ,
1976. He entered Weifang Medical College, Weifang, China, to pursue medicine and biomedical research from the year of 1994. After 3 years medical knowledge training, 2 years internship training and 3 years training in Immunology, he received M.S. in immunology and M.D. from Weifang Medical College. He joined to pursue his Ph.D. in Department of Bioscinece and Biotechnology at Drexel
University, Philadelphia, PA since 2002. From 2002 to 2007, he has been a research assistant working on Alzheimer’s disease on the cell molecular and biochemical level under the direction of Dr. Aleister Saunders. While Mr. Zhang was working on his Ph.D. thesis, he also collaborated with Dr. Irwin Chaiken’s research group from School of Medicine of Drexel University and characterized the anthrax invasion properties. Mr.Zhang’s research interests and specialties include a wide range of fields including neurodegenerative diseases especially
Alzheimer’s disease, immunology and infectious diseases. Mr. Zhang was also a teaching assistant in Department of Bioscience and Biotechnology at Drexel
University from 2002 to 2007. His teaching responsibilities include recitation and laboratory sessions in a number of curricular courses within biological fields. Mr.
Zhang also supervised research work of undergraduate students and early year graduate students. Most of his undergraduate students are pursuing higher degree in M.D. or Ph.D. Mr.Zhang’s research work has been published and
172 presented in several national and international conferences, including Society for
Neuroscience and international AD conferences. He will join Dr. Rudy Tanzi’s
research group at Mass General Hospital/ Harvard Medical School to continue
Alzheimer’s disease research in the late Fall of 2007.
Selective Publications and Presentations:
Zhang C, Khandelwal P, Cuellar T, Patel S, Cosentino C, Sarangi S, Lee J, Tanzi RE., Saunders AJ. An AICD-based Functional Screen to Identify APP Metabolism Regulators. Molecular Neurodegeneraton, in press, 2007 Cocklin S, Jost M, Robertson NM, Weeks SD, Weber HW, Young E, Seal S, Zhang C, Mosser E, Loll PJ, Saunders AJ, Rest RF, Chaiken IM. Real-time monitoring of the membrane-binding and insertion properties of the cholesterol- dependent cytolysin anthrolysin O from Bacillus anthracis. J Mol Recognit. 2006, 19(4):354-62. Zhang C, Khandelwal P., Saunders AJ. Identifying novel modulators of APP processing, Society for neuroscicne 35th conference, 2005. Zhang C, Saunders AJ. Alpha-secretase cleaveage pathway: a nonamyloidogenic pathway & a potential therapeutic pathway of Alzheimer’s disease. Manuscript in preparation. Invited review in Discovery Medicine, 2007 Zhang C, Saunders AJ, Characterization of Ubiquilin 1 functions in APP metabolism and its interaction with the proteasome system. Manuscript in preparation. 2007
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